Google Flu: CDC: ILI.log regression:: template2
bdanalytics
Date: (Mon) Jun 22, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/FluTrain.csv
New: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/FluTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: First run:
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/FluTrain.csv"
glb_newdt_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/FluTest.csv"
glb_out_pfx <- "template2_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- NULL # or TRUE or FALSE
glb_rsp_var_raw <- "ILI"
# for classification, the response variable has to be a factor
glb_rsp_var <- "ILI.log"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
return(log(raw))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(5.660867, 6.339272, 6.815222, 7.388359, 7.618892))## [1] 1.733577 1.846764 1.919159 1.999906 2.030631glb_map_rsp_var_to_raw <- function(var) {
return(exp(var))
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(5.660867, 6.339272, 6.815222, 7.388359, 7.618892)))## [1] 5.660867 6.339272 6.815222 7.388359 7.618892if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# "Week" - The range of dates represented by this observation, in year/month/day format.
#
# "ILI" - This column lists the percentage of ILI-related physician visits for the corresponding week.
#
# "Queries" - This column lists the fraction of queries that are ILI-related for the corresponding week, adjusted to be between 0 and 1 (higher values correspond to more ILI-related search queries).
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- c("Week")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
glb_derive_lst[["Week.bgn"]] <- list(
mapfn=function(Week) { return(substr(Week, 1, 10)) }
, args=c("Week"))
glb_derive_lst[["Week.end"]] <- list(
mapfn=function(Week) { return(substr(Week, 14, 23)) }
, args=c("Week"))
require(zoo)## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric# # If glb_allobs_df is not sorted in the desired manner
# glb_derive_lst[["ILI.2.lag"]] <- list(
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# , args=c("Week"))
glb_derive_lst[["ILI.2.lag"]] <- list(
mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
, args=c("ILI"))
glb_derive_lst[["ILI.2.lag.log"]] <- list(
mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
, args=c("ILI.2.lag"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# Add logs of numerics that are not distributed normally -> do automatically ???
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# args_lst <- NULL; for (arg in glb_derive_lst[["Week.bgn"]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; do.call(mapfn, args_lst)
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
glb_date_vars <- c("Week.bgn", "Week.end")
glb_date_fmts <- list(); glb_date_fmts[["Week.bgn"]] <- glb_date_fmts[["Week.end"]] <- "%Y-%m-%d";
glb_date_tzs <- list(); glb_date_tzs[["Week.bgn"]] <- glb_date_tzs[["Week.end"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- NULL # or c("<txt_var1>", "<txt_var2>")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- NULL # or c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("Week", "ILI.2.lag")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- TRUE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 6.863 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/FluTrain.csv..."
## [1] "dimensions of data in ./data/FluTrain.csv: 417 rows x 3 cols"
## Week ILI Queries
## 1 2004-01-04 - 2004-01-10 2.418331 0.2377158
## 2 2004-01-11 - 2004-01-17 1.809056 0.2204515
## 3 2004-01-18 - 2004-01-24 1.712024 0.2257636
## 4 2004-01-25 - 2004-01-31 1.542495 0.2377158
## 5 2004-02-01 - 2004-02-07 1.437868 0.2244356
## 6 2004-02-08 - 2004-02-14 1.324274 0.2071713
## Week ILI Queries
## 15 2004-04-11 - 2004-04-17 0.836130 0.07569721
## 63 2005-03-13 - 2005-03-19 2.673201 0.26560425
## 213 2008-01-27 - 2008-02-02 4.433810 0.41434263
## 303 2009-10-18 - 2009-10-24 7.618892 1.00000000
## 304 2009-10-25 - 2009-10-31 7.388359 0.92695883
## 411 2011-11-13 - 2011-11-19 1.462212 0.45551129
## Week ILI Queries
## 412 2011-11-20 - 2011-11-26 1.655415 0.4130146
## 413 2011-11-27 - 2011-12-03 1.465723 0.4780876
## 414 2011-12-04 - 2011-12-10 1.518106 0.4648074
## 415 2011-12-11 - 2011-12-17 1.663954 0.4794157
## 416 2011-12-18 - 2011-12-24 1.852736 0.5378486
## 417 2011-12-25 - 2011-12-31 2.124130 0.6188579
## 'data.frame': 417 obs. of 3 variables:
## $ Week : chr "2004-01-04 - 2004-01-10" "2004-01-11 - 2004-01-17" "2004-01-18 - 2004-01-24" "2004-01-25 - 2004-01-31" ...
## $ ILI : num 2.42 1.81 1.71 1.54 1.44 ...
## $ Queries: num 0.238 0.22 0.226 0.238 0.224 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/FluTest.csv..."
## [1] "dimensions of data in ./data/FluTest.csv: 52 rows x 3 cols"
## Week ILI Queries
## 1 2012-01-01 - 2012-01-07 1.766707 0.5936255
## 2 2012-01-08 - 2012-01-14 1.543401 0.4993360
## 3 2012-01-15 - 2012-01-21 1.647615 0.5006640
## 4 2012-01-22 - 2012-01-28 1.684297 0.4794157
## 5 2012-01-29 - 2012-02-04 1.863542 0.4714475
## 6 2012-02-05 - 2012-02-11 1.864079 0.5033201
## Week ILI Queries
## 1 2012-01-01 - 2012-01-07 1.766707 0.5936255
## 9 2012-02-26 - 2012-03-03 2.095549 0.4608234
## 20 2012-05-13 - 2012-05-19 1.266919 0.3027888
## 24 2012-06-10 - 2012-06-16 1.086121 0.2509960
## 49 2012-12-02 - 2012-12-08 2.978047 0.6719788
## 51 2012-12-16 - 2012-12-22 4.547268 0.7875166
## Week ILI Queries
## 47 2012-11-18 - 2012-11-24 2.304625 0.5112882
## 48 2012-11-25 - 2012-12-01 2.225997 0.6095618
## 49 2012-12-02 - 2012-12-08 2.978047 0.6719788
## 50 2012-12-09 - 2012-12-15 3.600230 0.7051793
## 51 2012-12-16 - 2012-12-22 4.547268 0.7875166
## 52 2012-12-23 - 2012-12-29 6.033614 0.8054209
## 'data.frame': 52 obs. of 3 variables:
## $ Week : chr "2012-01-01 - 2012-01-07" "2012-01-08 - 2012-01-14" "2012-01-15 - 2012-01-21" "2012-01-22 - 2012-01-28" ...
## $ ILI : num 1.77 1.54 1.65 1.68 1.86 ...
## $ Queries: num 0.594 0.499 0.501 0.479 0.471 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 6.863 7.173 0.31
## 2 inspect.data 2 0 7.173 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()
## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Week
## 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Week ILI Queries .src ILI.log
## 1 2004-01-04 - 2004-01-10 2.418331 0.2377158 Train 0.8830777
## 2 2004-01-11 - 2004-01-17 1.809056 0.2204515 Train 0.5928051
## 3 2004-01-18 - 2004-01-24 1.712024 0.2257636 Train 0.5376762
## 4 2004-01-25 - 2004-01-31 1.542495 0.2377158 Train 0.4334013
## 5 2004-02-01 - 2004-02-07 1.437868 0.2244356 Train 0.3631616
## 6 2004-02-08 - 2004-02-14 1.324274 0.2071713 Train 0.2808644
## Week ILI Queries .src ILI.log
## 153 2006-12-03 - 2006-12-09 1.8596834 0.3559097 Train 0.62040627
## 213 2008-01-27 - 2008-02-02 4.4338100 0.4143426 Train 1.48925927
## 300 2009-09-27 - 2009-10-03 4.6036164 0.6786189 Train 1.52684217
## 329 2010-04-18 - 2010-04-24 1.1620668 0.2602922 Train 0.15020011
## 447 2012-07-22 - 2012-07-28 0.9160412 0.2509960 Test -0.08769394
## 450 2012-08-12 - 2012-08-18 0.9017871 0.2695883 Test -0.10337676
## Week ILI Queries .src ILI.log
## 464 2012-11-18 - 2012-11-24 2.304625 0.5112882 Test 0.8349182
## 465 2012-11-25 - 2012-12-01 2.225997 0.6095618 Test 0.8002047
## 466 2012-12-02 - 2012-12-08 2.978047 0.6719788 Test 1.0912677
## 467 2012-12-09 - 2012-12-15 3.600230 0.7051793 Test 1.2809977
## 468 2012-12-16 - 2012-12-22 4.547268 0.7875166 Test 1.5145266
## 469 2012-12-23 - 2012-12-29 6.033614 0.8054209 Test 1.7973462
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: Queries"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 7.173 9.516 2.343
## 3 scrub.data 2 1 9.516 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ 0s in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## Week
## 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
# sel_obs <- function(Popular=NULL,
# NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
# Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
# Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
# perl=FALSE) {
sel_obs <- function(vars_lst) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 9.516 10.161 0.645
## 4 transform.data 2 2 10.161 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: Week.bgn..."
## [1] "Creating new feature: Week.end..."
## [1] "Creating new feature: ILI.2.lag..."
## [1] "Creating new feature: ILI.2.lag.log..."Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 10.161 10.224 0.063
## 5 extract.features 3 0 10.224 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 10.23 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}## Loading required package: XML## Warning in myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".POSIX")] >= : converting Week.bgn.wkend to class:factor## Week Week.bgn Week.bgn.zoo
## 1 2004-01-04 - 2004-01-10 2004-01-04 1073192400
## 2 2004-01-11 - 2004-01-17 2004-01-11 1073797200
## 3 2004-01-18 - 2004-01-24 2004-01-18 1074402000
## 4 2004-01-25 - 2004-01-31 2004-01-25 1075006800
## 5 2004-02-01 - 2004-02-07 2004-02-01 1075611600
## 6 2004-02-08 - 2004-02-14 2004-02-08 1076216400## Warning in myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".POSIX")] > : converting ILI.log to class:factor
## Don't know how to automatically pick scale for object of type zoo. Defaulting to continuous## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last1.log")] > : xcol_name:ILI.log is not a factor; creating ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last2.log")] > : xcol_name:ILI.log is not a factor; creating ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last10.log")] > : xcol_name:ILI.log is not a factor; creating
## ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last100.log")] > : xcol_name:ILI.log is not a factor; creating
## ILI.log_fctr
## Warning in myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".POSIX")] >= : converting Week.end.wkend to class:factor
## Week Week.end Week.end.zoo
## 1 2004-01-04 - 2004-01-10 2004-01-10 1073192400
## 2 2004-01-11 - 2004-01-17 2004-01-17 1073797200
## 3 2004-01-18 - 2004-01-24 2004-01-24 1074402000
## 4 2004-01-25 - 2004-01-31 2004-01-31 1075006800
## 5 2004-02-01 - 2004-02-07 2004-02-07 1075611600
## 6 2004-02-08 - 2004-02-14 2004-02-14 1076216400## Warning in myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".POSIX")] > : converting ILI.log to class:factor
## Don't know how to automatically pick scale for object of type zoo. Defaulting to continuous## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last1.log")] > : xcol_name:ILI.log is not a factor; creating ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last2.log")] > : xcol_name:ILI.log is not a factor; creating ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last10.log")] > : xcol_name:ILI.log is not a factor; creating
## ILI.log_fctr
## Warning in myplot_box(df = glb_allobs_df[glb_allobs_df[, paste0(feat,
## ".last100.log")] > : xcol_name:ILI.log is not a factor; creating
## ILI.log_fctr
rm(last1, last10, last100)
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn
## 1 extract.features_bgn 1 0 10.230
## 2 extract.features_factorize.str.vars 2 0 108.047
## end elapsed
## 1 108.046 97.816
## 2 NA NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## Week .src Week.bgn Week.end
## "Week" ".src" "Week.bgn" "Week.end"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}
if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[163, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_DTM' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_full_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_sprs_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_corpus' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn
## 2 extract.features_factorize.str.vars 2 0 108.047
## 3 extract.features_end 3 0 108.204
## end elapsed
## 2 108.203 0.157
## 3 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major step_minor bgn
## 1 extract.features_bgn 1 0 10.230
## 2 extract.features_factorize.str.vars 2 0 108.047
## end elapsed duration
## 1 108.046 97.816 97.816
## 2 108.203 0.157 0.156
## [1] "Total Elapsed Time: 108.203 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 10.224 109.764 99.54
## 6 cluster.data 4 0 109.765 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 109.765 127.266 17.501
## 7 manage.missing.data 4 1 127.267 NA NA# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## ILI.2.lag ILI.2.lag.log
## 2 2
## [1] "numeric data w/ 0s in : "
## Week.bgn.wkday.fctr Week.bgn.hour.fctr Week.bgn.minute.fctr
## 469 469 469
## Week.bgn.second.fctr Week.end.wkday.fctr Week.end.hour.fctr
## 469 469 469
## Week.end.minute.fctr Week.end.second.fctr Week.bgn.last1.log
## 469 469 1
## Week.bgn.last2.log Week.bgn.last10.log Week.bgn.last100.log
## 2 10 100
## Week.end.last1.log Week.end.last2.log Week.end.last10.log
## 1 2 10
## Week.end.last100.log
## 100
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Week Week.bgn Week.end
## 0 0 0# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
# complete(mice()) changes attributes of factors even though values don't change
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
} ## ILI.2.lag ILI.2.lag.log
## 2 2## Loading required package: mice
## Loading required package: Rcpp
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## Queries .rnorm ILI.2.lag.log Week.bgn.year.fctr
## Min. :0.04117 Min. :-2.63119 Min. :-0.6272 2006 : 53
## 1st Qu.:0.17928 1st Qu.:-0.64215 1st Qu.:-0.0770 2004 : 52
## Median :0.28685 Median : 0.03471 Median : 0.2403 2005 : 52
## Mean :0.29970 Mean :-0.01675 Mean : 0.3494 2007 : 52
## 3rd Qu.:0.39177 3rd Qu.: 0.67690 3rd Qu.: 0.7023 2008 : 52
## Max. :1.00000 Max. : 2.59377 Max. : 2.0306 2009 : 52
## NA's :2 (Other):156
## Week.bgn.month.fctr Week.bgn.date.fctr Week.bgn.wkday.fctr Week.bgn.wkend
## 01 : 41 (0.97,7]:108 0:469 Min. :1
## 05 : 41 (7,13] : 94 1st Qu.:1
## 07 : 41 (13,19] : 92 Median :1
## 10 : 41 (19,25] : 93 Mean :1
## 08 : 40 (25,31] : 82 3rd Qu.:1
## 04 : 39 Max. :1
## (Other):226
## Week.bgn.hour.fctr Week.bgn.minute.fctr Week.bgn.second.fctr
## Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0
##
## Week.end.year.fctr Week.end.month.fctr Week.end.date.fctr
## 2006 : 53 01 : 41 (0.97,7]:108
## 2004 : 52 05 : 41 (7,13] : 94
## 2005 : 52 07 : 41 (13,19] : 92
## 2007 : 52 10 : 41 (19,25] : 93
## 2008 : 52 08 : 40 (25,31] : 82
## 2009 : 52 04 : 39
## (Other):156 (Other):226
## Week.end.wkday.fctr Week.end.wkend Week.end.hour.fctr
## 0:469 Min. :1 Min. :0
## 1st Qu.:1 1st Qu.:0
## Median :1 Median :0
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
##
## Week.end.minute.fctr Week.end.second.fctr Week.bgn.last1.log
## Min. :0 Min. :0 Min. : 0.00
## 1st Qu.:0 1st Qu.:0 1st Qu.:13.31
## Median :0 Median :0 Median :13.31
## Mean :0 Mean :0 Mean :13.28
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:13.31
## Max. :0 Max. :0 Max. :13.32
##
## Week.bgn.last2.log Week.bgn.last10.log Week.bgn.last100.log
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:14.01 1st Qu.:15.62 1st Qu.:17.92
## Median :14.01 Median :15.62 Median :17.92
## Mean :13.95 Mean :15.28 Mean :14.10
## 3rd Qu.:14.01 3rd Qu.:15.62 3rd Qu.:17.92
## Max. :14.01 Max. :15.62 Max. :17.92
##
## Week.end.last1.log Week.end.last2.log Week.end.last10.log
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:13.31 1st Qu.:14.01 1st Qu.:15.62
## Median :13.31 Median :14.01 Median :15.62
## Mean :13.28 Mean :13.95 Mean :15.28
## 3rd Qu.:13.31 3rd Qu.:14.01 3rd Qu.:15.62
## Max. :13.32 Max. :14.01 Max. :15.62
##
## Week.end.last100.log
## Min. : 0.00
## 1st Qu.:17.92
## Median :17.92
## Mean :14.10
## 3rd Qu.:17.92
## Max. :17.92
##
##
## iter imp variable
## 1 1 ILI.2.lag.log
## 1 2 ILI.2.lag.log
## 1 3 ILI.2.lag.log
## 1 4 ILI.2.lag.log
## 1 5 ILI.2.lag.log
## 2 1 ILI.2.lag.log
## 2 2 ILI.2.lag.log
## 2 3 ILI.2.lag.log
## 2 4 ILI.2.lag.log
## 2 5 ILI.2.lag.log
## 3 1 ILI.2.lag.log
## 3 2 ILI.2.lag.log
## 3 3 ILI.2.lag.log
## 3 4 ILI.2.lag.log
## 3 5 ILI.2.lag.log
## 4 1 ILI.2.lag.log
## 4 2 ILI.2.lag.log
## 4 3 ILI.2.lag.log
## 4 4 ILI.2.lag.log
## 4 5 ILI.2.lag.log
## 5 1 ILI.2.lag.log
## 5 2 ILI.2.lag.log
## 5 3 ILI.2.lag.log
## 5 4 ILI.2.lag.log
## 5 5 ILI.2.lag.log
## Queries .rnorm ILI.2.lag.log
## Min. :0.04117 Min. :-2.63119 Min. :-0.62719
## 1st Qu.:0.17928 1st Qu.:-0.64215 1st Qu.:-0.07625
## Median :0.28685 Median : 0.03471 Median : 0.24476
## Mean :0.29970 Mean :-0.01675 Mean : 0.34952
## 3rd Qu.:0.39177 3rd Qu.: 0.67690 3rd Qu.: 0.70151
## Max. :1.00000 Max. : 2.59377 Max. : 2.03063
##
## Week.bgn.year.fctr Week.bgn.month.fctr Week.bgn.date.fctr
## 2006 : 53 01 : 41 (0.97,7]:108
## 2004 : 52 05 : 41 (7,13] : 94
## 2005 : 52 07 : 41 (13,19] : 92
## 2007 : 52 10 : 41 (19,25] : 93
## 2008 : 52 08 : 40 (25,31] : 82
## 2009 : 52 04 : 39
## (Other):156 (Other):226
## Week.bgn.wkday.fctr Week.bgn.wkend Week.bgn.hour.fctr
## 0:469 Min. :1 Min. :0
## 1st Qu.:1 1st Qu.:0
## Median :1 Median :0
## Mean :1 Mean :0
## 3rd Qu.:1 3rd Qu.:0
## Max. :1 Max. :0
##
## Week.bgn.minute.fctr Week.bgn.second.fctr Week.end.year.fctr
## Min. :0 Min. :0 2006 : 53
## 1st Qu.:0 1st Qu.:0 2004 : 52
## Median :0 Median :0 2005 : 52
## Mean :0 Mean :0 2007 : 52
## 3rd Qu.:0 3rd Qu.:0 2008 : 52
## Max. :0 Max. :0 2009 : 52
## (Other):156
## Week.end.month.fctr Week.end.date.fctr Week.end.wkday.fctr Week.end.wkend
## 01 : 41 (0.97,7]:108 0:469 Min. :1
## 05 : 41 (7,13] : 94 1st Qu.:1
## 07 : 41 (13,19] : 92 Median :1
## 10 : 41 (19,25] : 93 Mean :1
## 08 : 40 (25,31] : 82 3rd Qu.:1
## 04 : 39 Max. :1
## (Other):226
## Week.end.hour.fctr Week.end.minute.fctr Week.end.second.fctr
## Min. :0 Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :0 Median :0 Median :0
## Mean :0 Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0 Max. :0
##
## Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:13.31 1st Qu.:14.01 1st Qu.:15.62
## Median :13.31 Median :14.01 Median :15.62
## Mean :13.28 Mean :13.95 Mean :15.28
## 3rd Qu.:13.31 3rd Qu.:14.01 3rd Qu.:15.62
## Max. :13.32 Max. :14.01 Max. :15.62
##
## Week.bgn.last100.log Week.end.last1.log Week.end.last2.log
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:17.92 1st Qu.:13.31 1st Qu.:14.01
## Median :17.92 Median :13.31 Median :14.01
## Mean :14.10 Mean :13.28 Mean :13.95
## 3rd Qu.:17.92 3rd Qu.:13.31 3rd Qu.:14.01
## Max. :17.92 Max. :13.32 Max. :14.01
##
## Week.end.last10.log Week.end.last100.log
## Min. : 0.00 Min. : 0.00
## 1st Qu.:15.62 1st Qu.:17.92
## Median :15.62 Median :17.92
## Mean :15.28 Mean :14.10
## 3rd Qu.:15.62 3rd Qu.:17.92
## Max. :15.62 Max. :17.92
## mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## ILI.2.lag ILI.2.lag.log
## 2 2
## [1] "numeric data w/ 0s in : "
## Week.bgn.wkday.fctr Week.bgn.hour.fctr Week.bgn.minute.fctr
## 469 469 469
## Week.bgn.second.fctr Week.end.wkday.fctr Week.end.hour.fctr
## 469 469 469
## Week.end.minute.fctr Week.end.second.fctr Week.bgn.last1.log
## 469 469 1
## Week.bgn.last2.log Week.bgn.last10.log Week.bgn.last100.log
## 2 10 100
## Week.end.last1.log Week.end.last2.log Week.end.last10.log
## 1 2 10
## Week.end.last100.log
## 100
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Week Week.bgn Week.end
## 0 0 0Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 127.267 128.257 0.99
## 8 select.features 5 0 128.258 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## ILI ILI 0.9451682372
## ILI.2.lag.log ILI.2.lag.log 0.9214355113
## ILI.2.lag.log.nonNA ILI.2.lag.log.nonNA 0.9204139777
## ILI.2.lag ILI.2.lag 0.8591322165
## Queries Queries 0.8420332864
## Week.bgn.last100.log Week.bgn.last100.log 0.2000557734
## Week.end.last100.log Week.end.last100.log 0.2000557734
## Week.bgn.year.fctr Week.bgn.year.fctr 0.1987250820
## Week.end.year.fctr Week.end.year.fctr 0.1987250820
## Week.bgn.year.fctr.nonNA Week.bgn.year.fctr.nonNA 0.1987250820
## Week.bgn.month.fctr Week.bgn.month.fctr -0.1979117680
## Week.end.month.fctr Week.end.month.fctr -0.1979117680
## Week.bgn.month.fctr.nonNA Week.bgn.month.fctr.nonNA -0.1979117680
## Week.bgn.POSIX Week.bgn.POSIX 0.1730840769
## Week.end.POSIX Week.end.POSIX 0.1730840769
## Week.bgn.zoo Week.bgn.zoo 0.1730840769
## Week.end.zoo Week.end.zoo 0.1730840769
## Week.bgn.last2.log Week.bgn.last2.log -0.0488875907
## Week.end.last2.log Week.end.last2.log -0.0488875907
## Week.bgn.last1.log Week.bgn.last1.log -0.0473819651
## Week.end.last1.log Week.end.last1.log -0.0473819651
## .rnorm .rnorm 0.0400349990
## Week.bgn.date.fctr Week.bgn.date.fctr 0.0088237363
## Week.end.date.fctr Week.end.date.fctr 0.0088237363
## Week.bgn.date.fctr.nonNA Week.bgn.date.fctr.nonNA 0.0088237363
## Week.bgn.last10.log Week.bgn.last10.log 0.0006098826
## Week.end.last10.log Week.end.last10.log 0.0006098826
## Week.bgn.wkend Week.bgn.wkend NA
## Week.bgn.hour.fctr Week.bgn.hour.fctr NA
## Week.bgn.minute.fctr Week.bgn.minute.fctr NA
## Week.bgn.second.fctr Week.bgn.second.fctr NA
## Week.end.wkend Week.end.wkend NA
## Week.end.hour.fctr Week.end.hour.fctr NA
## Week.end.minute.fctr Week.end.minute.fctr NA
## Week.end.second.fctr Week.end.second.fctr NA
## Week.bgn.wkday.fctr Week.bgn.wkday.fctr NA
## Week.end.wkday.fctr Week.end.wkday.fctr NA
## exclude.as.feat cor.y.abs
## ILI 1 0.9451682372
## ILI.2.lag.log 1 0.9214355113
## ILI.2.lag.log.nonNA 0 0.9204139777
## ILI.2.lag 1 0.8591322165
## Queries 0 0.8420332864
## Week.bgn.last100.log 0 0.2000557734
## Week.end.last100.log 0 0.2000557734
## Week.bgn.year.fctr 1 0.1987250820
## Week.end.year.fctr 0 0.1987250820
## Week.bgn.year.fctr.nonNA 0 0.1987250820
## Week.bgn.month.fctr 1 0.1979117680
## Week.end.month.fctr 0 0.1979117680
## Week.bgn.month.fctr.nonNA 0 0.1979117680
## Week.bgn.POSIX 1 0.1730840769
## Week.end.POSIX 1 0.1730840769
## Week.bgn.zoo 1 0.1730840769
## Week.end.zoo 1 0.1730840769
## Week.bgn.last2.log 0 0.0488875907
## Week.end.last2.log 0 0.0488875907
## Week.bgn.last1.log 0 0.0473819651
## Week.end.last1.log 0 0.0473819651
## .rnorm 0 0.0400349990
## Week.bgn.date.fctr 1 0.0088237363
## Week.end.date.fctr 0 0.0088237363
## Week.bgn.date.fctr.nonNA 0 0.0088237363
## Week.bgn.last10.log 0 0.0006098826
## Week.end.last10.log 0 0.0006098826
## Week.bgn.wkend 0 NA
## Week.bgn.hour.fctr 0 NA
## Week.bgn.minute.fctr 0 NA
## Week.bgn.second.fctr 0 NA
## Week.end.wkend 0 NA
## Week.end.hour.fctr 0 NA
## Week.end.minute.fctr 0 NA
## Week.end.second.fctr 0 NA
## Week.bgn.wkday.fctr 0 NA
## Week.end.wkday.fctr 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## Loading required package: reshape2## [1] "cor(Week.bgn.last1.log, Week.end.last1.log)=1.0000"
## [1] "cor(ILI.log, Week.bgn.last1.log)=-0.0474"
## [1] "cor(ILI.log, Week.end.last1.log)=-0.0474"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.end.last1.log as highly correlated with
## Week.bgn.last1.log## [1] "cor(Week.bgn.last100.log, Week.end.last100.log)=1.0000"
## [1] "cor(ILI.log, Week.bgn.last100.log)=0.2001"
## [1] "cor(ILI.log, Week.end.last100.log)=0.2001"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.end.last100.log as highly correlated with
## Week.bgn.last100.log## [1] "cor(Week.bgn.last2.log, Week.end.last2.log)=1.0000"
## [1] "cor(ILI.log, Week.bgn.last2.log)=-0.0489"
## [1] "cor(ILI.log, Week.end.last2.log)=-0.0489"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.end.last2.log as highly correlated with
## Week.bgn.last2.log## [1] "cor(Week.bgn.month.fctr.nonNA, Week.end.month.fctr)=1.0000"
## [1] "cor(ILI.log, Week.bgn.month.fctr.nonNA)=-0.1979"
## [1] "cor(ILI.log, Week.end.month.fctr)=-0.1979"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.end.month.fctr as highly correlated with
## Week.bgn.month.fctr.nonNA## [1] "cor(Week.bgn.year.fctr.nonNA, Week.end.year.fctr)=1.0000"
## [1] "cor(ILI.log, Week.bgn.year.fctr.nonNA)=0.1987"
## [1] "cor(ILI.log, Week.end.year.fctr)=0.1987"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.end.year.fctr as highly correlated with
## Week.bgn.year.fctr.nonNA## [1] "cor(ILI.2.lag.log.nonNA, Queries)=0.7424"
## [1] "cor(ILI.log, ILI.2.lag.log.nonNA)=0.9204"
## [1] "cor(ILI.log, Queries)=0.8420"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Queries as highly correlated with ILI.
## 2.lag.log.nonNA## [1] "cor(Week.bgn.last100.log, Week.bgn.year.fctr.nonNA)=0.7399"
## [1] "cor(ILI.log, Week.bgn.last100.log)=0.2001"
## [1] "cor(ILI.log, Week.bgn.year.fctr.nonNA)=0.1987"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.bgn.year.fctr.nonNA as highly correlated
## with Week.bgn.last100.log## [1] "cor(Week.bgn.last1.log, Week.bgn.last2.log)=0.7063"
## [1] "cor(ILI.log, Week.bgn.last1.log)=-0.0474"
## [1] "cor(ILI.log, Week.bgn.last2.log)=-0.0489"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Week.bgn.last1.log as highly correlated with
## Week.bgn.last2.log## id cor.y exclude.as.feat cor.y.abs
## 2 ILI 0.9451682372 1 0.9451682372
## 4 ILI.2.lag.log 0.9214355113 1 0.9214355113
## 5 ILI.2.lag.log.nonNA 0.9204139777 0 0.9204139777
## 3 ILI.2.lag 0.8591322165 1 0.8591322165
## 6 Queries 0.8420332864 0 0.8420332864
## 12 Week.bgn.last100.log 0.2000557734 0 0.2000557734
## 28 Week.end.last100.log 0.2000557734 0 0.2000557734
## 21 Week.bgn.year.fctr 0.1987250820 1 0.1987250820
## 22 Week.bgn.year.fctr.nonNA 0.1987250820 0 0.1987250820
## 36 Week.end.year.fctr 0.1987250820 0 0.1987250820
## 17 Week.bgn.POSIX 0.1730840769 1 0.1730840769
## 23 Week.bgn.zoo 0.1730840769 1 0.1730840769
## 32 Week.end.POSIX 0.1730840769 1 0.1730840769
## 37 Week.end.zoo 0.1730840769 1 0.1730840769
## 1 .rnorm 0.0400349990 0 0.0400349990
## 7 Week.bgn.date.fctr 0.0088237363 1 0.0088237363
## 8 Week.bgn.date.fctr.nonNA 0.0088237363 0 0.0088237363
## 24 Week.end.date.fctr 0.0088237363 0 0.0088237363
## 11 Week.bgn.last10.log 0.0006098826 0 0.0006098826
## 27 Week.end.last10.log 0.0006098826 0 0.0006098826
## 10 Week.bgn.last1.log -0.0473819651 0 0.0473819651
## 26 Week.end.last1.log -0.0473819651 0 0.0473819651
## 13 Week.bgn.last2.log -0.0488875907 0 0.0488875907
## 29 Week.end.last2.log -0.0488875907 0 0.0488875907
## 15 Week.bgn.month.fctr -0.1979117680 1 0.1979117680
## 16 Week.bgn.month.fctr.nonNA -0.1979117680 0 0.1979117680
## 31 Week.end.month.fctr -0.1979117680 0 0.1979117680
## 9 Week.bgn.hour.fctr NA 0 NA
## 14 Week.bgn.minute.fctr NA 0 NA
## 18 Week.bgn.second.fctr NA 0 NA
## 19 Week.bgn.wkday.fctr NA 0 NA
## 20 Week.bgn.wkend NA 0 NA
## 25 Week.end.hour.fctr NA 0 NA
## 30 Week.end.minute.fctr NA 0 NA
## 33 Week.end.second.fctr NA 0 NA
## 34 Week.end.wkday.fctr NA 0 NA
## 35 Week.end.wkend NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 2 <NA> 1.000000 100.0000000 FALSE FALSE
## 4 <NA> 1.000000 99.5203837 FALSE FALSE
## 5 <NA> 2.000000 99.7601918 FALSE FALSE
## 3 <NA> 1.000000 99.5203837 FALSE FALSE
## 6 ILI.2.lag.log.nonNA 1.250000 62.5899281 FALSE FALSE
## 12 <NA> 2.690000 0.9592326 FALSE FALSE
## 28 Week.bgn.last100.log 2.690000 0.9592326 FALSE FALSE
## 21 <NA> 1.019231 1.9184652 FALSE FALSE
## 22 Week.bgn.last100.log 1.019231 1.9184652 FALSE FALSE
## 36 Week.bgn.year.fctr.nonNA 1.019231 1.9184652 FALSE FALSE
## 17 <NA> 1.000000 100.0000000 FALSE FALSE
## 23 <NA> 1.000000 100.0000000 FALSE FALSE
## 32 <NA> 1.000000 100.0000000 FALSE FALSE
## 37 <NA> 1.000000 100.0000000 FALSE FALSE
## 1 <NA> 1.000000 100.0000000 FALSE FALSE
## 7 <NA> 1.156627 1.1990408 FALSE FALSE
## 8 <NA> 1.156627 1.1990408 FALSE FALSE
## 24 <NA> 1.156627 1.1990408 FALSE FALSE
## 11 <NA> 3.125000 0.9592326 FALSE FALSE
## 27 <NA> 3.125000 0.9592326 FALSE FALSE
## 10 Week.bgn.last2.log 50.000000 0.9592326 FALSE TRUE
## 26 Week.bgn.last1.log 50.000000 0.9592326 FALSE TRUE
## 13 <NA> 23.937500 0.9592326 FALSE TRUE
## 29 Week.bgn.last2.log 23.937500 0.9592326 FALSE TRUE
## 15 <NA> 1.000000 2.8776978 FALSE FALSE
## 16 <NA> 1.000000 2.8776978 FALSE FALSE
## 31 Week.bgn.month.fctr.nonNA 1.000000 2.8776978 FALSE FALSE
## 9 <NA> 0.000000 0.2398082 TRUE TRUE
## 14 <NA> 0.000000 0.2398082 TRUE TRUE
## 18 <NA> 0.000000 0.2398082 TRUE TRUE
## 19 <NA> 0.000000 0.2398082 TRUE TRUE
## 20 <NA> 0.000000 0.2398082 TRUE TRUE
## 25 <NA> 0.000000 0.2398082 TRUE TRUE
## 30 <NA> 0.000000 0.2398082 TRUE TRUE
## 33 <NA> 0.000000 0.2398082 TRUE TRUE
## 34 <NA> 0.000000 0.2398082 TRUE TRUE
## 35 <NA> 0.000000 0.2398082 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 2 FALSE FALSE
## 4 FALSE FALSE
## 5 FALSE FALSE
## 3 FALSE FALSE
## 6 FALSE FALSE
## 12 FALSE FALSE
## 28 FALSE FALSE
## 21 FALSE FALSE
## 22 FALSE FALSE
## 36 FALSE FALSE
## 17 FALSE FALSE
## 23 FALSE FALSE
## 32 FALSE FALSE
## 37 FALSE FALSE
## 1 FALSE FALSE
## 7 FALSE TRUE
## 8 FALSE TRUE
## 24 FALSE TRUE
## 11 FALSE TRUE
## 27 FALSE TRUE
## 10 FALSE FALSE
## 26 FALSE FALSE
## 13 FALSE FALSE
## 29 FALSE FALSE
## 15 FALSE FALSE
## 16 FALSE FALSE
## 31 FALSE FALSE
## 9 TRUE NA
## 14 TRUE NA
## 18 TRUE NA
## 19 TRUE NA
## 20 TRUE NA
## 25 TRUE NA
## 30 TRUE NA
## 33 TRUE NA
## 34 TRUE NA
## 35 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 10 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 10 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 10 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 9 Week.bgn.hour.fctr NA 0 NA <NA>
## 14 Week.bgn.minute.fctr NA 0 NA <NA>
## 18 Week.bgn.second.fctr NA 0 NA <NA>
## 19 Week.bgn.wkday.fctr NA 0 NA <NA>
## 20 Week.bgn.wkend NA 0 NA <NA>
## 25 Week.end.hour.fctr NA 0 NA <NA>
## 30 Week.end.minute.fctr NA 0 NA <NA>
## 33 Week.end.second.fctr NA 0 NA <NA>
## 34 Week.end.wkday.fctr NA 0 NA <NA>
## 35 Week.end.wkend NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 9 0 0.2398082 TRUE TRUE TRUE NA
## 14 0 0.2398082 TRUE TRUE TRUE NA
## 18 0 0.2398082 TRUE TRUE TRUE NA
## 19 0 0.2398082 TRUE TRUE TRUE NA
## 20 0 0.2398082 TRUE TRUE TRUE NA
## 25 0 0.2398082 TRUE TRUE TRUE NA
## 30 0 0.2398082 TRUE TRUE TRUE NA
## 33 0 0.2398082 TRUE TRUE TRUE NA
## 34 0 0.2398082 TRUE TRUE TRUE NA
## 35 0 0.2398082 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## ILI.2.lag ILI.2.lag.log
## 2 2
## [1] "numeric data w/ 0s in : "
## Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 1 2 10
## Week.bgn.last100.log Week.end.last1.log Week.end.last2.log
## 100 1 2
## Week.end.last10.log Week.end.last100.log
## 10 100
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Week Week.bgn Week.end
## 0 0 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 128.258 128.999 0.742
## 9 partition.data.training 6 0 129.000 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for ILI.log; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 37 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## ILI.log ILI.log TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## 2 ILI 0.9451682 TRUE 0.9451682 <NA> 1
## ILI.log ILI.log NA TRUE NA <NA> NA
## percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 2 100 FALSE FALSE FALSE FALSE
## ILI.log NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 2 NA TRUE NA NA
## ILI.log NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "Week.bgn.hour.fctr" "Week.bgn.minute.fctr" "Week.bgn.second.fctr"
## [4] "Week.bgn.wkday.fctr" "Week.bgn.wkend" "Week.end.hour.fctr"
## [7] "Week.end.minute.fctr" "Week.end.second.fctr" "Week.end.wkday.fctr"
## [10] "Week.end.wkend"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 469 33print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 417 42print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 417 42print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 52 42print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 52 42# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 129.000 129.347 0.347
## 10 fit.models 7 0 129.347 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9780 -0.4535 -0.1217 0.3793 1.6924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34737 0.02717 12.784 <2e-16 ***
## .rnorm 0.02347 0.02875 0.816 0.415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5548 on 415 degrees of freedom
## Multiple R-squared: 0.001603, Adjusted R-squared: -0.000803
## F-statistic: 0.6662 on 1 and 415 DF, p-value: 0.4148
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.566
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.002 0.001602801 0.5534848 -0.01575461
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.4080698 -0.0008029752if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log"## Loading required package: rpart## Fitting cp = 0.635 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 417
##
## CP nsplit rel error
## 1 0.6353105 0 1
##
## Node number 1: 417 observations
## mean=0.3476702, MSE=0.3068372
##
## n= 417
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 417 127.9511 0.3476702 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Max.cor.Y.cv.0.rpart rpart
## feats max.nTuningRuns
## 1 ILI.2.lag.log.nonNA, Week.bgn.last100.log 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.547 0.016 0
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5539289 0 0.4048928ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 417
##
## CP nsplit rel error
## 1 6.353105e-01 0 1.0000000
## 2 9.235980e-02 1 0.3646895
## 3 6.881570e-02 2 0.2723297
## 4 2.714155e-02 3 0.2035140
## 5 9.739677e-03 4 0.1763725
## 6 9.553531e-03 5 0.1666328
## 7 8.328212e-03 6 0.1570793
## 8 4.575803e-03 7 0.1487511
## 9 2.026905e-03 8 0.1441753
## 10 1.920792e-03 9 0.1421484
## 11 1.557624e-03 10 0.1402276
## 12 1.495507e-03 12 0.1371123
## 13 1.379224e-03 14 0.1341213
## 14 1.366310e-03 15 0.1327421
## 15 9.483927e-04 16 0.1313758
## 16 8.321743e-04 17 0.1304274
## 17 8.288942e-04 19 0.1287630
## 18 4.238163e-04 20 0.1279341
## 19 3.274522e-04 21 0.1275103
## 20 3.176373e-04 22 0.1271829
## 21 2.254809e-04 24 0.1265476
## 22 2.223878e-04 25 0.1263221
## 23 1.518224e-04 26 0.1260997
## 24 1.222121e-04 27 0.1259479
## 25 8.532486e-05 28 0.1258257
## 26 0.000000e+00 29 0.1257404
##
## Variable importance
## ILI.2.lag.log.nonNA Week.bgn.last100.log
## 92 8
##
## Node number 1: 417 observations, complexity param=0.6353105
## mean=0.3476702, MSE=0.3068372
## left son=2 (238 obs) right son=3 (179 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.3642121 to the left, improve=0.6353105, (0 missing)
## Week.bgn.last100.log < 17.91785 to the left, improve=0.0514081, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the left, agree=0.619, adj=0.112, (0 split)
##
## Node number 2: 238 observations, complexity param=0.0688157
## mean=-0.03522948, MSE=0.07240075
## left son=4 (135 obs) right son=5 (103 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.005288628 to the left, improve=0.51098900, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.01577173, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91779 to the right, agree=0.601, adj=0.078, (0 split)
##
## Node number 3: 179 observations, complexity param=0.0923598
## mean=0.8567771, MSE=0.1644193
## left son=6 (141 obs) right son=7 (38 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.160636 to the left, improve=0.40153300, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.02044471, (0 missing)
##
## Node number 4: 135 observations, complexity param=0.009739677
## mean=-0.2032371, MSE=0.02669874
## left son=8 (46 obs) right son=9 (89 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.2557664 to the left, improve=0.34575160, (0 missing)
## Week.bgn.last100.log < 8.958882 to the left, improve=0.01554574, (0 missing)
##
## Node number 5: 103 observations, complexity param=0.008328212
## mean=0.1849747, MSE=0.04681557
## left son=10 (66 obs) right son=11 (37 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.1886472 to the left, improve=0.220987800, (0 missing)
## Week.bgn.last100.log < 8.958882 to the left, improve=0.003546195, (0 missing)
##
## Node number 6: 141 observations, complexity param=0.02714155
## mean=0.7233883, MSE=0.098441
## left son=12 (84 obs) right son=13 (57 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.7871896 to the left, improve=0.25019790, (0 missing)
## Week.bgn.last100.log < 17.91785 to the left, improve=0.00108719, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the left, agree=0.603, adj=0.018, (0 split)
##
## Node number 7: 38 observations, complexity param=0.009553531
## mean=1.35172, MSE=0.09824558
## left son=14 (24 obs) right son=15 (14 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.485112 to the left, improve=0.3274247, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.1548345, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91779 to the right, agree=0.711, adj=0.214, (0 split)
##
## Node number 8: 46 observations, complexity param=0.001379224
## mean=-0.3368793, MSE=0.01754249
## left son=16 (28 obs) right son=17 (18 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.3364944 to the left, improve=0.2186906000, (0 missing)
## Week.bgn.last100.log < 8.958912 to the left, improve=0.0001459021, (0 missing)
##
## Node number 9: 89 observations, complexity param=0.00136631
## mean=-0.1341636, MSE=0.0174289
## left son=18 (53 obs) right son=19 (36 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.1187063 to the left, improve=0.11270240, (0 missing)
## Week.bgn.last100.log < 8.958882 to the left, improve=0.02457038, (0 missing)
##
## Node number 10: 66 observations, complexity param=0.002026905
## mean=0.108818, MSE=0.02579353
## left son=20 (35 obs) right son=21 (31 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.1054685 to the left, improve=0.152343000, (0 missing)
## Week.bgn.last100.log < 8.958882 to the left, improve=0.008691956, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91779 to the left, agree=0.652, adj=0.258, (0 split)
##
## Node number 11: 37 observations, complexity param=0.0009483927
## mean=0.3208218, MSE=0.05551424
## left son=22 (18 obs) right son=23 (19 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.2672436 to the right, improve=0.05907805, (0 missing)
## Week.bgn.last100.log < 8.958882 to the left, improve=0.01362272, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 8.958882 to the left, agree=0.595, adj=0.167, (0 split)
##
## Node number 12: 84 observations, complexity param=0.004575803
## mean=0.5941095, MSE=0.07154071
## left son=24 (26 obs) right son=25 (58 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.4780125 to the left, improve=0.0974268900, (0 missing)
## Week.bgn.last100.log < 8.958912 to the left, improve=0.0007104286, (0 missing)
##
## Node number 13: 57 observations, complexity param=0.0008321743
## mean=0.9139046, MSE=0.07715736
## left son=26 (45 obs) right son=27 (12 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.8769581 to the right, improve=0.01875118, (0 missing)
## Week.bgn.last100.log < 17.91785 to the right, improve=0.00163561, (0 missing)
##
## Node number 14: 24 observations, complexity param=0.001920792
## mean=1.214735, MSE=0.06238431
## left son=28 (14 obs) right son=29 (10 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.367642 to the left, improve=0.1641489, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 8.958912 to the right, agree=0.625, adj=0.1, (0 split)
##
## Node number 15: 14 observations
## mean=1.58655, MSE=0.07240882
##
## Node number 16: 28 observations, complexity param=0.0002223878
## mean=-0.3865406, MSE=0.01641498
## left son=32 (15 obs) right son=33 (13 obs)
## Primary splits:
## Week.bgn.last100.log < 8.958912 to the right, improve=0.06190942, (0 missing)
## ILI.2.lag.log.nonNA < -0.4027548 to the left, improve=0.04806694, (0 missing)
## Surrogate splits:
## ILI.2.lag.log.nonNA < -0.3889181 to the left, agree=0.643, adj=0.231, (0 split)
##
## Node number 17: 18 observations
## mean=-0.2596284, MSE=0.009492319
##
## Node number 18: 53 observations, complexity param=0.0002254809
## mean=-0.1706907, MSE=0.01735997
## left son=36 (19 obs) right son=37 (34 obs)
## Primary splits:
## Week.bgn.last100.log < 8.958882 to the left, improve=0.03135659, (0 missing)
## ILI.2.lag.log.nonNA < -0.2037076 to the right, improve=0.02610092, (0 missing)
## Surrogate splits:
## ILI.2.lag.log.nonNA < -0.2068175 to the left, agree=0.698, adj=0.158, (0 split)
##
## Node number 19: 36 observations, complexity param=8.532486e-05
## mean=-0.08038768, MSE=0.01267426
## left son=38 (18 obs) right son=39 (18 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.06643646 to the left, improve=0.023927350, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.006995762, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91779 to the left, agree=0.528, adj=0.056, (0 split)
##
## Node number 20: 35 observations, complexity param=0.0004238163
## mean=0.04982323, MSE=0.01327698
## left son=40 (18 obs) right son=41 (17 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.05575614 to the left, improve=0.11669560, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.07230438, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 8.958882 to the left, agree=0.571, adj=0.118, (0 split)
##
## Node number 21: 31 observations, complexity param=0.0003176373
## mean=0.175425, MSE=0.03155917
## left son=42 (11 obs) right son=43 (20 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.1561068 to the right, improve=0.03380649, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.00791479, (0 missing)
##
## Node number 22: 18 observations
## mean=0.2619841, MSE=0.04141873
##
## Node number 23: 19 observations
## mean=0.3765628, MSE=0.06248115
##
## Node number 24: 26 observations, complexity param=0.0008288942
## mean=0.4694161, MSE=0.04158716
## left son=48 (8 obs) right son=49 (18 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.4304485 to the right, improve=0.09808679, (0 missing)
##
## Node number 25: 58 observations, complexity param=0.001557624
## mean=0.6500065, MSE=0.0748737
## left son=50 (35 obs) right son=51 (23 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.6743843 to the left, improve=0.03720445, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the left, agree=0.672, adj=0.174, (0 split)
##
## Node number 26: 45 observations, complexity param=0.0008321743
## mean=0.8942625, MSE=0.07023539
## left son=52 (27 obs) right son=53 (18 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.026878 to the left, improve=0.0412859700, (0 missing)
## Week.bgn.last100.log < 17.91785 to the right, improve=0.0002826772, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the left, agree=0.622, adj=0.056, (0 split)
##
## Node number 27: 12 observations
## mean=0.9875624, MSE=0.09624252
##
## Node number 28: 14 observations
## mean=1.12921, MSE=0.04984909
##
## Node number 29: 10 observations
## mean=1.33447, MSE=0.05535686
##
## Node number 32: 15 observations
## mean=-0.4162179, MSE=0.01511771
##
## Node number 33: 13 observations
## mean=-0.3522975, MSE=0.01572299
##
## Node number 36: 19 observations
## mean=-0.2019013, MSE=0.01969646
##
## Node number 37: 34 observations, complexity param=0.0001518224
## mean=-0.1532495, MSE=0.01520574
## left son=74 (9 obs) right son=75 (25 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.1486436 to the right, improve=0.03757451, (0 missing)
##
## Node number 38: 18 observations
## mean=-0.09780208, MSE=0.01277056
##
## Node number 39: 18 observations
## mean=-0.06297328, MSE=0.01197143
##
## Node number 40: 18 observations
## mean=0.01157027, MSE=0.01121365
##
## Node number 41: 17 observations
## mean=0.09032636, MSE=0.01227181
##
## Node number 42: 11 observations
## mean=0.1313815, MSE=0.01006617
##
## Node number 43: 20 observations, complexity param=0.0003176373
## mean=0.1996489, MSE=0.04172662
## left son=86 (13 obs) right son=87 (7 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.1363493 to the left, improve=0.05776892, (0 missing)
##
## Node number 48: 8 observations
## mean=0.3736137, MSE=0.02161138
##
## Node number 49: 18 observations
## mean=0.5119949, MSE=0.04457318
##
## Node number 50: 35 observations, complexity param=0.001495507
## mean=0.6072214, MSE=0.07330728
## left son=100 (24 obs) right son=101 (11 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.5104656 to the right, improve=0.07044408, (0 missing)
##
## Node number 51: 23 observations, complexity param=0.001557624
## mean=0.7151142, MSE=0.07023273
## left son=102 (13 obs) right son=103 (10 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.7008803 to the right, improve=0.1467373, (0 missing)
##
## Node number 52: 27 observations, complexity param=0.0003274522
## mean=0.8502948, MSE=0.07441528
## left son=104 (8 obs) right son=105 (19 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.9822524 to the right, improve=0.02085288, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the right, agree=0.741, adj=0.125, (0 split)
##
## Node number 53: 18 observations
## mean=0.960214, MSE=0.05671621
##
## Node number 74: 9 observations
## mean=-0.1930877, MSE=0.003911875
##
## Node number 75: 25 observations, complexity param=0.0001222121
## mean=-0.1389078, MSE=0.0184945
## left son=150 (8 obs) right son=151 (17 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < -0.2185118 to the left, improve=0.03382015, (0 missing)
##
## Node number 86: 13 observations
## mean=0.1636217, MSE=0.02154578
##
## Node number 87: 7 observations
## mean=0.2665567, MSE=0.07231819
##
## Node number 100: 24 observations, complexity param=0.001495507
## mean=0.558571, MSE=0.05159449
## left son=200 (7 obs) right son=201 (17 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.5560231 to the left, improve=0.1630998, (0 missing)
##
## Node number 101: 11 observations
## mean=0.7133678, MSE=0.1042495
##
## Node number 102: 13 observations
## mean=0.6260776, MSE=0.06166083
##
## Node number 103: 10 observations
## mean=0.8308617, MSE=0.05767296
##
## Node number 104: 8 observations
## mean=0.7895868, MSE=0.03874549
##
## Node number 105: 19 observations
## mean=0.8758561, MSE=0.08722899
##
## Node number 150: 8 observations
## mean=-0.1753654, MSE=0.009977913
##
## Node number 151: 17 observations
## mean=-0.1217513, MSE=0.02158247
##
## Node number 200: 7 observations
## mean=0.4156146, MSE=0.01711153
##
## Node number 201: 17 observations
## mean=0.6174355, MSE=0.05391327
##
## n= 417
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 417 127.95110000 0.34767020
## 2) ILI.2.lag.log.nonNA< 0.3642121 238 17.23138000 -0.03522948
## 4) ILI.2.lag.log.nonNA< -0.005288628 135 3.60432900 -0.20323710
## 8) ILI.2.lag.log.nonNA< -0.2557664 46 0.80695440 -0.33687930
## 16) ILI.2.lag.log.nonNA< -0.3364944 28 0.45961930 -0.38654060
## 32) Week.bgn.last100.log>=8.958912 15 0.22676570 -0.41621790 *
## 33) Week.bgn.last100.log< 8.958912 13 0.20439890 -0.35229750 *
## 17) ILI.2.lag.log.nonNA>=-0.3364944 18 0.17086170 -0.25962840 *
## 9) ILI.2.lag.log.nonNA>=-0.2557664 89 1.55117300 -0.13416360
## 18) ILI.2.lag.log.nonNA< -0.1187063 53 0.92007850 -0.17069070
## 36) Week.bgn.last100.log< 8.958882 19 0.37423280 -0.20190130 *
## 37) Week.bgn.last100.log>=8.958882 34 0.51699520 -0.15324950
## 74) ILI.2.lag.log.nonNA>=-0.1486436 9 0.03520688 -0.19308770 *
## 75) ILI.2.lag.log.nonNA< -0.1486436 25 0.46236240 -0.13890780
## 150) ILI.2.lag.log.nonNA< -0.2185118 8 0.07982330 -0.17536540 *
## 151) ILI.2.lag.log.nonNA>=-0.2185118 17 0.36690200 -0.12175130 *
## 19) ILI.2.lag.log.nonNA>=-0.1187063 36 0.45627320 -0.08038768
## 38) ILI.2.lag.log.nonNA< -0.06643646 18 0.22987000 -0.09780208 *
## 39) ILI.2.lag.log.nonNA>=-0.06643646 18 0.21548580 -0.06297328 *
## 5) ILI.2.lag.log.nonNA>=-0.005288628 103 4.82200400 0.18497470
## 10) ILI.2.lag.log.nonNA< 0.1886472 66 1.70237300 0.10881800
## 20) ILI.2.lag.log.nonNA< 0.1054685 35 0.46469410 0.04982323
## 40) ILI.2.lag.log.nonNA< 0.05575614 18 0.20184570 0.01157027 *
## 41) ILI.2.lag.log.nonNA>=0.05575614 17 0.20862070 0.09032636 *
## 21) ILI.2.lag.log.nonNA>=0.1054685 31 0.97833440 0.17542500
## 42) ILI.2.lag.log.nonNA>=0.1561068 11 0.11072790 0.13138150 *
## 43) ILI.2.lag.log.nonNA< 0.1561068 20 0.83453250 0.19964890
## 86) ILI.2.lag.log.nonNA< 0.1363493 13 0.28009510 0.16362170 *
## 87) ILI.2.lag.log.nonNA>=0.1363493 7 0.50622730 0.26655670 *
## 11) ILI.2.lag.log.nonNA>=0.1886472 37 2.05402700 0.32082180
## 22) ILI.2.lag.log.nonNA>=0.2672436 18 0.74553720 0.26198410 *
## 23) ILI.2.lag.log.nonNA< 0.2672436 19 1.18714200 0.37656280 *
## 3) ILI.2.lag.log.nonNA>=0.3642121 179 29.43105000 0.85677710
## 6) ILI.2.lag.log.nonNA< 1.160636 141 13.88018000 0.72338830
## 12) ILI.2.lag.log.nonNA< 0.7871896 84 6.00942000 0.59410950
## 24) ILI.2.lag.log.nonNA< 0.4780125 26 1.08126600 0.46941610
## 48) ILI.2.lag.log.nonNA>=0.4304485 8 0.17289100 0.37361370 *
## 49) ILI.2.lag.log.nonNA< 0.4304485 18 0.80231720 0.51199490 *
## 25) ILI.2.lag.log.nonNA>=0.4780125 58 4.34267400 0.65000650
## 50) ILI.2.lag.log.nonNA< 0.6743843 35 2.56575500 0.60722140
## 100) ILI.2.lag.log.nonNA>=0.5104656 24 1.23826800 0.55857100
## 200) ILI.2.lag.log.nonNA< 0.5560231 7 0.11978070 0.41561460 *
## 201) ILI.2.lag.log.nonNA>=0.5560231 17 0.91652560 0.61743550 *
## 101) ILI.2.lag.log.nonNA< 0.5104656 11 1.14674500 0.71336780 *
## 51) ILI.2.lag.log.nonNA>=0.6743843 23 1.61535300 0.71511420
## 102) ILI.2.lag.log.nonNA>=0.7008803 13 0.80159070 0.62607760 *
## 103) ILI.2.lag.log.nonNA< 0.7008803 10 0.57672960 0.83086170 *
## 13) ILI.2.lag.log.nonNA>=0.7871896 57 4.39797000 0.91390460
## 26) ILI.2.lag.log.nonNA>=0.8769581 45 3.16059200 0.89426250
## 52) ILI.2.lag.log.nonNA< 1.026878 27 2.00921300 0.85029480
## 104) ILI.2.lag.log.nonNA>=0.9822524 8 0.30996400 0.78958680 *
## 105) ILI.2.lag.log.nonNA< 0.9822524 19 1.65735100 0.87585610 *
## 53) ILI.2.lag.log.nonNA>=1.026878 18 1.02089200 0.96021400 *
## 27) ILI.2.lag.log.nonNA< 0.8769581 12 1.15491000 0.98756240 *
## 7) ILI.2.lag.log.nonNA>=1.160636 38 3.73333200 1.35172000
## 14) ILI.2.lag.log.nonNA< 1.485112 24 1.49722300 1.21473500
## 28) ILI.2.lag.log.nonNA< 1.367642 14 0.69788730 1.12921000 *
## 29) ILI.2.lag.log.nonNA>=1.367642 10 0.55356860 1.33447000 *
## 15) ILI.2.lag.log.nonNA>=1.485112 14 1.01372400 1.58655000 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart
## feats max.nTuningRuns
## 1 ILI.2.lag.log.nonNA, Week.bgn.last100.log 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.476 0.013 0.8742596
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.1964226 0.7442082 0.204778if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0688 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 417
##
## CP nsplit rel error
## 1 0.6353105 0 1.0000000
## 2 0.0923598 1 0.3646895
## 3 0.0688157 2 0.2723297
##
## Variable importance
## ILI.2.lag.log.nonNA Week.bgn.last100.log
## 91 9
##
## Node number 1: 417 observations, complexity param=0.6353105
## mean=0.3476702, MSE=0.3068372
## left son=2 (238 obs) right son=3 (179 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.3642121 to the left, improve=0.6353105, (0 missing)
## Week.bgn.last100.log < 17.91785 to the left, improve=0.0514081, (0 missing)
## Surrogate splits:
## Week.bgn.last100.log < 17.91785 to the left, agree=0.619, adj=0.112, (0 split)
##
## Node number 2: 238 observations
## mean=-0.03522948, MSE=0.07240075
##
## Node number 3: 179 observations, complexity param=0.0923598
## mean=0.8567771, MSE=0.1644193
## left son=6 (141 obs) right son=7 (38 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.160636 to the left, improve=0.40153300, (0 missing)
## Week.bgn.last100.log < 17.91779 to the right, improve=0.02044471, (0 missing)
##
## Node number 6: 141 observations
## mean=0.7233883, MSE=0.098441
##
## Node number 7: 38 observations
## mean=1.35172, MSE=0.09824558
##
## n= 417
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 417 127.951100 0.34767020
## 2) ILI.2.lag.log.nonNA< 0.3642121 238 17.231380 -0.03522948 *
## 3) ILI.2.lag.log.nonNA>=0.3642121 179 29.431050 0.85677710
## 6) ILI.2.lag.log.nonNA< 1.160636 141 13.880180 0.72338830 *
## 7) ILI.2.lag.log.nonNA>=1.160636 38 3.733332 1.35172000 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.rpart rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.045 0.014
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.7276703 0.305688 0.5715219 0.2650357 0.7000844
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.03621604 0.0873017# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45573 -0.16134 -0.02313 0.11680 0.82385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.013593 0.021911 0.620 0.535
## ILI.2.lag.log.nonNA 0.917923 0.019611 46.806 <2e-16 ***
## Week.bgn.last100.log 0.001200 0.001419 0.846 0.398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2172 on 414 degrees of freedom
## Multiple R-squared: 0.8474, Adjusted R-squared: 0.8467
## F-statistic: 1150 on 2 and 414 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.lm lm ILI.2.lag.log.nonNA, Week.bgn.last100.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.894 0.002
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.8474256 0.2172781 0.8008593 0.1806841 0.8466885
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8456678 0.01629213 0.03979421if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}## Warning in grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep =
## ""), : argument 'pattern' has length > 1 and only the first element will be
## used# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49287 -0.12271 -0.00814 0.09731 0.93015
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 0.018213 0.021403
## ILI.2.lag.log.nonNA 2.024306 0.449141
## Week.bgn.last100.log 0.002023 0.001367
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 0.017678 0.010910
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 0.148444 0.100875
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.211701 0.219974
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -0.230738 0.220369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.177708 0.221369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -0.260484 0.217276
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -0.243517 0.221416
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -0.234396 0.220642
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -0.073575 0.045252
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.010988 0.057602
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.025315 0.049117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -0.241012 0.050936
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -0.483300 0.081461
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 0.099225 0.098290
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -0.230973 0.097117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.091939 0.119895
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 0.148733 0.112588
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.083180 0.086770
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 0.200677 0.078522
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.067658 0.068132
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 0.163824 0.066647
## t value Pr(>|t|)
## (Intercept) 0.851 0.3953
## ILI.2.lag.log.nonNA 4.507 8.68e-06 ***
## Week.bgn.last100.log 1.480 0.1397
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 1.620 0.1059
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 1.472 0.1419
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.962 0.3364
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -1.047 0.2957
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.803 0.4226
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -1.199 0.2313
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -1.100 0.2721
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -1.062 0.2887
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -1.626 0.1048
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.191 0.8488
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.515 0.6066
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -4.732 3.11e-06 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -5.933 6.54e-09 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 1.010 0.3133
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -2.378 0.0179 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.767 0.4436
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 1.321 0.1873
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.959 0.3383
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 2.556 0.0110 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.993 0.3213
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 2.458 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1906 on 393 degrees of freedom
## Multiple R-squared: 0.8884, Adjusted R-squared: 0.8818
## F-statistic: 136 on 23 and 393 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.901 0.01
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.8883614 2.744383 0.8869087 0.1361616 0.8818278
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3980416 3.981253 0.4354334# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last2.log, Week.bgn.month.fctr.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49176 -0.09908 -0.02105 0.06266 0.97631
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4238511 0.1263135 3.356 0.000869 ***
## ILI.2.lag.log.nonNA 0.8670850 0.0245238 35.357 < 2e-16 ***
## Week.bgn.last100.log 0.0007402 0.0012222 0.606 0.545139
## .rnorm -0.0042796 0.0093738 -0.457 0.648244
## Week.bgn.date.fctr.nonNA2 -0.0083818 0.0265841 -0.315 0.752705
## Week.bgn.date.fctr.nonNA3 0.0236838 0.0268024 0.884 0.377424
## Week.bgn.date.fctr.nonNA4 0.0293814 0.0267301 1.099 0.272353
## Week.bgn.date.fctr.nonNA5 0.0388797 0.0275316 1.412 0.158681
## `Week.end.date.fctr(7,13]` NA NA NA NA
## `Week.end.date.fctr(13,19]` NA NA NA NA
## `Week.end.date.fctr(19,25]` NA NA NA NA
## `Week.end.date.fctr(25,31]` NA NA NA NA
## Week.bgn.last10.log 0.0166568 0.0044772 3.720 0.000228 ***
## Week.end.last10.log NA NA NA NA
## Week.bgn.last2.log -0.0402170 0.0102724 -3.915 0.000106 ***
## Week.bgn.month.fctr.nonNA2 0.0526295 0.0441268 1.193 0.233706
## Week.bgn.month.fctr.nonNA3 -0.2544180 0.0431501 -5.896 7.98e-09 ***
## Week.bgn.month.fctr.nonNA4 -0.2787947 0.0449952 -6.196 1.45e-09 ***
## Week.bgn.month.fctr.nonNA5 -0.1140584 0.0460397 -2.477 0.013651 *
## Week.bgn.month.fctr.nonNA6 -0.2857019 0.0477678 -5.981 4.96e-09 ***
## Week.bgn.month.fctr.nonNA7 -0.2289393 0.0499140 -4.587 6.05e-06 ***
## Week.bgn.month.fctr.nonNA8 -0.0875292 0.0506637 -1.728 0.084830 .
## Week.bgn.month.fctr.nonNA9 -0.0415082 0.0486449 -0.853 0.394014
## Week.bgn.month.fctr.nonNA10 0.0129504 0.0451944 0.287 0.774607
## Week.bgn.month.fctr.nonNA11 -0.0208782 0.0442255 -0.472 0.637126
## Week.bgn.month.fctr.nonNA12 0.0966740 0.0437413 2.210 0.027667 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1772 on 396 degrees of freedom
## Multiple R-squared: 0.9028, Adjusted R-squared: 0.8979
## F-statistic: 183.9 on 20 and 396 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 ILI.2.lag.log.nonNA, Week.bgn.last100.log, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last2.log, Week.bgn.month.fctr.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.905 0.01
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9028104 0.1858368 0.8799352 0.1402968 0.8979018
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8905275 0.02000994 0.02855833rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 129.347 145.145 15.798
## 11 fit.models 7 1 145.145 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 147.577 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 147.577 147.591 0.014
## 2 fit.models_1_lm 2 0 147.592 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.44670 -0.08553 -0.00315 0.06802 0.68025
##
## Coefficients: (28 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.307809 0.152075 2.024 0.043650 *
## ILI.2.lag.log.nonNA 0.539252 0.033974 15.872 < 2e-16 ***
## Queries 1.443489 0.126061 11.451 < 2e-16 ***
## Week.bgn.last100.log 0.007966 0.004655 1.711 0.087854 .
## Week.end.last100.log NA NA NA NA
## Week.bgn.year.fctr.nonNA2 0.006957 0.032882 0.212 0.832542
## Week.bgn.year.fctr.nonNA3 -0.123304 0.089531 -1.377 0.169239
## Week.bgn.year.fctr.nonNA4 -0.177282 0.089341 -1.984 0.047926 *
## Week.bgn.year.fctr.nonNA5 -0.157908 0.089833 -1.758 0.079573 .
## Week.bgn.year.fctr.nonNA6 -0.122828 0.093380 -1.315 0.189167
## Week.bgn.year.fctr.nonNA7 -0.269157 0.090353 -2.979 0.003075 **
## Week.bgn.year.fctr.nonNA8 -0.304090 0.090975 -3.343 0.000911 ***
## Week.bgn.year.fctr.nonNA9 NA NA NA NA
## Week.end.year.fctr2005 NA NA NA NA
## Week.end.year.fctr2006 NA NA NA NA
## Week.end.year.fctr2007 NA NA NA NA
## Week.end.year.fctr2008 NA NA NA NA
## Week.end.year.fctr2009 NA NA NA NA
## Week.end.year.fctr2010 NA NA NA NA
## Week.end.year.fctr2011 NA NA NA NA
## Week.end.year.fctr2012 NA NA NA NA
## .rnorm -0.006227 0.008019 -0.777 0.437916
## Week.bgn.date.fctr.nonNA2 0.008102 0.022702 0.357 0.721386
## Week.bgn.date.fctr.nonNA3 0.020264 0.022760 0.890 0.373854
## Week.bgn.date.fctr.nonNA4 0.032501 0.022695 1.432 0.152934
## Week.bgn.date.fctr.nonNA5 0.020456 0.023449 0.872 0.383563
## `Week.end.date.fctr(7,13]` NA NA NA NA
## `Week.end.date.fctr(13,19]` NA NA NA NA
## `Week.end.date.fctr(19,25]` NA NA NA NA
## `Week.end.date.fctr(25,31]` NA NA NA NA
## Week.bgn.last10.log 0.014859 0.003981 3.733 0.000218 ***
## Week.end.last10.log NA NA NA NA
## Week.bgn.last1.log -0.015451 0.016052 -0.963 0.336391
## Week.end.last1.log NA NA NA NA
## Week.bgn.last2.log -0.035227 0.011587 -3.040 0.002525 **
## Week.end.last2.log NA NA NA NA
## Week.bgn.month.fctr.nonNA2 0.123809 0.037870 3.269 0.001175 **
## Week.bgn.month.fctr.nonNA3 -0.050204 0.040735 -1.232 0.218520
## Week.bgn.month.fctr.nonNA4 -0.136562 0.041911 -3.258 0.001219 **
## Week.bgn.month.fctr.nonNA5 -0.001851 0.043435 -0.043 0.966027
## Week.bgn.month.fctr.nonNA6 -0.145283 0.045953 -3.162 0.001693 **
## Week.bgn.month.fctr.nonNA7 -0.167452 0.046920 -3.569 0.000404 ***
## Week.bgn.month.fctr.nonNA8 -0.082832 0.047103 -1.759 0.079451 .
## Week.bgn.month.fctr.nonNA9 -0.113525 0.043241 -2.625 0.008997 **
## Week.bgn.month.fctr.nonNA10 -0.124632 0.040198 -3.100 0.002074 **
## Week.bgn.month.fctr.nonNA11 -0.086368 0.038130 -2.265 0.024058 *
## Week.bgn.month.fctr.nonNA12 -0.058992 0.039944 -1.477 0.140526
## Week.end.month.fctr02 NA NA NA NA
## Week.end.month.fctr03 NA NA NA NA
## Week.end.month.fctr04 NA NA NA NA
## Week.end.month.fctr05 NA NA NA NA
## Week.end.month.fctr06 NA NA NA NA
## Week.end.month.fctr07 NA NA NA NA
## Week.end.month.fctr08 NA NA NA NA
## Week.end.month.fctr09 NA NA NA NA
## Week.end.month.fctr10 NA NA NA NA
## Week.end.month.fctr11 NA NA NA NA
## Week.end.month.fctr12 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1502 on 387 degrees of freedom
## Multiple R-squared: 0.9318, Adjusted R-squared: 0.9266
## F-statistic: 182.2 on 29 and 387 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.977 0.019
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9317513 3.454309 0.327414 0.332058 0.9266371
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3062242 2.969678 0.5263306
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 147.592 150.085 2.493
## 3 fit.models_1_glm 3 0 150.086 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.44670 -0.08553 -0.00315 0.06802 0.68025
##
## Coefficients: (28 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.307809 0.152075 2.024 0.043650 *
## ILI.2.lag.log.nonNA 0.539252 0.033974 15.872 < 2e-16 ***
## Queries 1.443489 0.126061 11.451 < 2e-16 ***
## Week.bgn.last100.log 0.007966 0.004655 1.711 0.087854 .
## Week.end.last100.log NA NA NA NA
## Week.bgn.year.fctr.nonNA2 0.006957 0.032882 0.212 0.832542
## Week.bgn.year.fctr.nonNA3 -0.123304 0.089531 -1.377 0.169239
## Week.bgn.year.fctr.nonNA4 -0.177282 0.089341 -1.984 0.047926 *
## Week.bgn.year.fctr.nonNA5 -0.157908 0.089833 -1.758 0.079573 .
## Week.bgn.year.fctr.nonNA6 -0.122828 0.093380 -1.315 0.189167
## Week.bgn.year.fctr.nonNA7 -0.269157 0.090353 -2.979 0.003075 **
## Week.bgn.year.fctr.nonNA8 -0.304090 0.090975 -3.343 0.000911 ***
## Week.bgn.year.fctr.nonNA9 NA NA NA NA
## Week.end.year.fctr2005 NA NA NA NA
## Week.end.year.fctr2006 NA NA NA NA
## Week.end.year.fctr2007 NA NA NA NA
## Week.end.year.fctr2008 NA NA NA NA
## Week.end.year.fctr2009 NA NA NA NA
## Week.end.year.fctr2010 NA NA NA NA
## Week.end.year.fctr2011 NA NA NA NA
## Week.end.year.fctr2012 NA NA NA NA
## .rnorm -0.006227 0.008019 -0.777 0.437916
## Week.bgn.date.fctr.nonNA2 0.008102 0.022702 0.357 0.721386
## Week.bgn.date.fctr.nonNA3 0.020264 0.022760 0.890 0.373854
## Week.bgn.date.fctr.nonNA4 0.032501 0.022695 1.432 0.152934
## Week.bgn.date.fctr.nonNA5 0.020456 0.023449 0.872 0.383563
## `Week.end.date.fctr(7,13]` NA NA NA NA
## `Week.end.date.fctr(13,19]` NA NA NA NA
## `Week.end.date.fctr(19,25]` NA NA NA NA
## `Week.end.date.fctr(25,31]` NA NA NA NA
## Week.bgn.last10.log 0.014859 0.003981 3.733 0.000218 ***
## Week.end.last10.log NA NA NA NA
## Week.bgn.last1.log -0.015451 0.016052 -0.963 0.336391
## Week.end.last1.log NA NA NA NA
## Week.bgn.last2.log -0.035227 0.011587 -3.040 0.002525 **
## Week.end.last2.log NA NA NA NA
## Week.bgn.month.fctr.nonNA2 0.123809 0.037870 3.269 0.001175 **
## Week.bgn.month.fctr.nonNA3 -0.050204 0.040735 -1.232 0.218520
## Week.bgn.month.fctr.nonNA4 -0.136562 0.041911 -3.258 0.001219 **
## Week.bgn.month.fctr.nonNA5 -0.001851 0.043435 -0.043 0.966027
## Week.bgn.month.fctr.nonNA6 -0.145283 0.045953 -3.162 0.001693 **
## Week.bgn.month.fctr.nonNA7 -0.167452 0.046920 -3.569 0.000404 ***
## Week.bgn.month.fctr.nonNA8 -0.082832 0.047103 -1.759 0.079451 .
## Week.bgn.month.fctr.nonNA9 -0.113525 0.043241 -2.625 0.008997 **
## Week.bgn.month.fctr.nonNA10 -0.124632 0.040198 -3.100 0.002074 **
## Week.bgn.month.fctr.nonNA11 -0.086368 0.038130 -2.265 0.024058 *
## Week.bgn.month.fctr.nonNA12 -0.058992 0.039944 -1.477 0.140526
## Week.end.month.fctr02 NA NA NA NA
## Week.end.month.fctr03 NA NA NA NA
## Week.end.month.fctr04 NA NA NA NA
## Week.end.month.fctr05 NA NA NA NA
## Week.end.month.fctr06 NA NA NA NA
## Week.end.month.fctr07 NA NA NA NA
## Week.end.month.fctr08 NA NA NA NA
## Week.end.month.fctr09 NA NA NA NA
## Week.end.month.fctr10 NA NA NA NA
## Week.end.month.fctr11 NA NA NA NA
## Week.end.month.fctr12 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.02256459)
##
## Null deviance: 127.9511 on 416 degrees of freedom
## Residual deviance: 8.7325 on 387 degrees of freedom
## AIC: -366.74
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## model_id model_method
## 1 All.X.glm glm
## feats
## 1 ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.03 0.083
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.9317513 3.454309 0.327414 0.332058 -366.7419
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3062242 2.969678 0.5263306
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 150.086 152.705 2.619
## 4 fit.models_1_bayesglm 4 0 152.705 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-5, built: 2015-05-13)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/HW2_CDC_Google_Flu## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.44572 -0.08624 -0.00336 0.06790 0.68002
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.308556 0.157887 1.954 0.0514 .
## ILI.2.lag.log.nonNA 0.539923 0.035243 15.320 <2e-16 ***
## Queries 1.439142 0.130652 11.015 <2e-16 ***
## Week.bgn.last100.log 0.003961 1.662214 0.002 0.9981
## Week.end.last100.log 0.003961 1.662214 0.002 0.9981
## Week.bgn.year.fctr.nonNA2 0.003563 1.662403 0.002 0.9983
## Week.bgn.year.fctr.nonNA3 -0.061232 1.663553 -0.037 0.9707
## Week.bgn.year.fctr.nonNA4 -0.088169 1.663984 -0.053 0.9578
## Week.bgn.year.fctr.nonNA5 -0.078468 1.663818 -0.047 0.9624
## Week.bgn.year.fctr.nonNA6 -0.060749 1.663622 -0.037 0.9709
## Week.bgn.year.fctr.nonNA7 -0.133938 1.665100 -0.080 0.9359
## Week.bgn.year.fctr.nonNA8 -0.151354 1.665648 -0.091 0.9276
## Week.bgn.year.fctr.nonNA9 0.000000 2.879119 0.000 1.0000
## Week.end.year.fctr2005 0.003563 1.662403 0.002 0.9983
## Week.end.year.fctr2006 -0.061232 1.663553 -0.037 0.9707
## Week.end.year.fctr2007 -0.088169 1.663984 -0.053 0.9578
## Week.end.year.fctr2008 -0.078468 1.663818 -0.047 0.9624
## Week.end.year.fctr2009 -0.060749 1.663622 -0.037 0.9709
## Week.end.year.fctr2010 -0.133938 1.665100 -0.080 0.9359
## Week.end.year.fctr2011 -0.151354 1.665648 -0.091 0.9276
## Week.end.year.fctr2012 0.000000 2.879119 0.000 1.0000
## .rnorm -0.006215 0.008325 -0.747 0.4558
## Week.bgn.date.fctr.nonNA2 0.004029 1.662343 0.002 0.9981
## Week.bgn.date.fctr.nonNA3 0.010134 1.662352 0.006 0.9951
## Week.bgn.date.fctr.nonNA4 0.016246 1.662370 0.010 0.9922
## Week.bgn.date.fctr.nonNA5 0.010252 1.662356 0.006 0.9951
## `Week.end.date.fctr(7,13]` 0.004029 1.662343 0.002 0.9981
## `Week.end.date.fctr(13,19]` 0.010134 1.662352 0.006 0.9951
## `Week.end.date.fctr(19,25]` 0.016246 1.662370 0.010 0.9922
## `Week.end.date.fctr(25,31]` 0.010252 1.662356 0.006 0.9951
## Week.bgn.last10.log 0.007432 1.662220 0.004 0.9964
## Week.end.last10.log 0.007432 1.662220 0.004 0.9964
## Week.bgn.last1.log -0.007724 1.662288 -0.005 0.9963
## Week.end.last1.log -0.007724 1.662288 -0.005 0.9963
## Week.bgn.last2.log -0.017612 1.662278 -0.011 0.9916
## Week.end.last2.log -0.017612 1.662278 -0.011 0.9916
## Week.bgn.month.fctr.nonNA2 0.061852 1.662854 0.037 0.9703
## Week.bgn.month.fctr.nonNA3 -0.025372 1.662534 -0.015 0.9878
## Week.bgn.month.fctr.nonNA4 -0.068533 1.662982 -0.041 0.9672
## Week.bgn.month.fctr.nonNA5 -0.001180 1.662489 -0.001 0.9994
## Week.bgn.month.fctr.nonNA6 -0.072942 1.663088 -0.044 0.9650
## Week.bgn.month.fctr.nonNA7 -0.083944 1.663284 -0.050 0.9598
## Week.bgn.month.fctr.nonNA8 -0.041565 1.662712 -0.025 0.9801
## Week.bgn.month.fctr.nonNA9 -0.056751 1.662835 -0.034 0.9728
## Week.bgn.month.fctr.nonNA10 -0.062169 1.662877 -0.037 0.9702
## Week.bgn.month.fctr.nonNA11 -0.043112 1.662643 -0.026 0.9793
## Week.bgn.month.fctr.nonNA12 -0.029243 1.662550 -0.018 0.9860
## Week.end.month.fctr02 0.061852 1.662854 0.037 0.9703
## Week.end.month.fctr03 -0.025372 1.662534 -0.015 0.9878
## Week.end.month.fctr04 -0.068533 1.662982 -0.041 0.9672
## Week.end.month.fctr05 -0.001180 1.662489 -0.001 0.9994
## Week.end.month.fctr06 -0.072942 1.663088 -0.044 0.9650
## Week.end.month.fctr07 -0.083944 1.663284 -0.050 0.9598
## Week.end.month.fctr08 -0.041565 1.662712 -0.025 0.9801
## Week.end.month.fctr09 -0.056751 1.662835 -0.034 0.9728
## Week.end.month.fctr10 -0.062169 1.662877 -0.037 0.9702
## Week.end.month.fctr11 -0.043112 1.662643 -0.026 0.9793
## Week.end.month.fctr12 -0.029243 1.662550 -0.018 0.9860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.02432458)
##
## Null deviance: 127.9511 on 416 degrees of freedom
## Residual deviance: 8.7325 on 359 degrees of freedom
## AIC: -310.74
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.669 0.148
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.9317511 0.2484433 0.3333059 0.3306004 -310.7405
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.7906456 0.1107067 0.1744511
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_bayesglm 4 0 152.705 155.339 2.634
## 5 fit.models_1_rpart 5 0 155.340 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0688 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 417
##
## CP nsplit rel error
## 1 0.6353105 0 1.0000000
## 2 0.0923598 1 0.3646895
## 3 0.0688157 2 0.2723297
##
## Variable importance
## ILI.2.lag.log.nonNA Queries
## 43 22
## Week.bgn.last10.log Week.end.last10.log
## 9 9
## Week.bgn.year.fctr.nonNA6 Week.end.year.fctr2009
## 8 8
## Week.bgn.month.fctr.nonNA10 Week.end.month.fctr10
## 1 1
##
## Node number 1: 417 observations, complexity param=0.6353105
## mean=0.3476702, MSE=0.3068372
## left son=2 (238 obs) right son=3 (179 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 0.3642121 to the left, improve=0.6353105, (0 missing)
## Queries < 0.2848606 to the left, improve=0.5265077, (0 missing)
## Week.bgn.year.fctr.nonNA6 < 0.5 to the left, improve=0.1640573, (0 missing)
## Week.end.year.fctr2009 < 0.5 to the left, improve=0.1640573, (0 missing)
## Week.bgn.month.fctr.nonNA2 < 0.5 to the left, improve=0.1356085, (0 missing)
## Surrogate splits:
## Queries < 0.3233732 to the left, agree=0.808, adj=0.553, (0 split)
## Week.bgn.last10.log < 15.61554 to the left, agree=0.674, adj=0.240, (0 split)
## Week.end.last10.log < 15.61554 to the left, agree=0.674, adj=0.240, (0 split)
## Week.bgn.year.fctr.nonNA6 < 0.5 to the left, agree=0.657, adj=0.201, (0 split)
## Week.end.year.fctr2009 < 0.5 to the left, agree=0.657, adj=0.201, (0 split)
##
## Node number 2: 238 observations
## mean=-0.03522948, MSE=0.07240075
##
## Node number 3: 179 observations, complexity param=0.0923598
## mean=0.8567771, MSE=0.1644193
## left son=6 (141 obs) right son=7 (38 obs)
## Primary splits:
## ILI.2.lag.log.nonNA < 1.160636 to the left, improve=0.4015330, (0 missing)
## Queries < 0.5053121 to the left, improve=0.3302168, (0 missing)
## Week.bgn.last10.log < 15.61494 to the left, improve=0.2225811, (0 missing)
## Week.end.last10.log < 15.61494 to the left, improve=0.2225811, (0 missing)
## Week.bgn.month.fctr.nonNA4 < 0.5 to the right, improve=0.2102918, (0 missing)
## Surrogate splits:
## Queries < 0.5471448 to the left, agree=0.838, adj=0.237, (0 split)
## Week.bgn.month.fctr.nonNA10 < 0.5 to the left, agree=0.810, adj=0.105, (0 split)
## Week.end.month.fctr10 < 0.5 to the left, agree=0.810, adj=0.105, (0 split)
## Week.bgn.month.fctr.nonNA9 < 0.5 to the left, agree=0.799, adj=0.053, (0 split)
## Week.end.month.fctr09 < 0.5 to the left, agree=0.799, adj=0.053, (0 split)
##
## Node number 6: 141 observations
## mean=0.7233883, MSE=0.098441
##
## Node number 7: 38 observations
## mean=1.35172, MSE=0.09824558
##
## n= 417
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 417 127.951100 0.34767020
## 2) ILI.2.lag.log.nonNA< 0.3642121 238 17.231380 -0.03522948 *
## 3) ILI.2.lag.log.nonNA>=0.3642121 179 29.431050 0.85677710
## 6) ILI.2.lag.log.nonNA< 1.160636 141 13.880180 0.72338830 *
## 7) ILI.2.lag.log.nonNA>=1.160636 38 3.733332 1.35172000 *
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.121 0.08
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.7276703 0.305688 0.5715219 0.2650357 0.7000844
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.03621604 0.0873017
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 155.340 158.458 3.118
## 6 fit.models_1_rf 6 0 158.458 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 29 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 417 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 417 -none- numeric
## importance 56 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 417 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 56 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## [1] " calling mypredict_mdl for OOB:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.date.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 6.443 1.857
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9869949 0.1574949 0.7833803 0.1885728 0.9189164
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.01511178 0.02617253# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
#model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats), 1)
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
model_id <- "Flu.Trend2"; indep_vars_vctr <- c("Queries", "ILI.2.lag.log.nonNA")
for (method in c("lm")) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: Flu.Trend2.lm"
## [1] " indep_vars: Queries, ILI.2.lag.log.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52096 -0.11299 -0.01915 0.08230 0.76446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.23599 0.01978 -11.93 <2e-16 ***
## Queries 1.24372 0.08022 15.50 <2e-16 ***
## ILI.2.lag.log.nonNA 0.65847 0.02283 28.84 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1729 on 414 degrees of freedom
## Multiple R-squared: 0.9033, Adjusted R-squared: 0.9028
## F-statistic: 1934 on 2 and 414 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Flu.Trend2.lm lm Queries, ILI.2.lag.log.nonNA 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.863 0.003 0.9033037
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 0.1729956 0.8565912 0.1533303 0.9028365 0.9020136
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.0135587 0.02723253
## importance
## ILI.2.lag.log.nonNA 100
## Queries 0# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## Flu.Trend2.lm Flu.Trend2.lm lm
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.cv.0.cp.0.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Interact.High.cor.Y.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA
## Low.cor.X.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last2.log, Week.bgn.month.fctr.nonNA
## All.X.lm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.glm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.bayesglm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.no.rnorm.rpart ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.no.rnorm.rf ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## Flu.Trend2.lm Queries, ILI.2.lag.log.nonNA
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.566
## Max.cor.Y.cv.0.rpart 0 0.547
## Max.cor.Y.cv.0.cp.0.rpart 0 0.476
## Max.cor.Y.rpart 3 1.045
## Max.cor.Y.lm 1 0.894
## Interact.High.cor.Y.lm 1 0.901
## Low.cor.X.lm 1 0.905
## All.X.lm 1 0.977
## All.X.glm 1 1.030
## All.X.bayesglm 1 1.669
## All.X.no.rnorm.rpart 3 1.121
## All.X.no.rnorm.rf 3 6.443
## Flu.Trend2.lm 1 0.863
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit
## MFO.lm 0.002 0.001602801 0.5534848
## Max.cor.Y.cv.0.rpart 0.016 0.000000000 0.5539289
## Max.cor.Y.cv.0.cp.0.rpart 0.013 0.874259639 0.1964226
## Max.cor.Y.rpart 0.014 0.727670264 0.3056880
## Max.cor.Y.lm 0.002 0.847425566 0.2172781
## Interact.High.cor.Y.lm 0.010 0.888361359 2.7443835
## Low.cor.X.lm 0.010 0.902810393 0.1858368
## All.X.lm 0.019 0.931751316 3.4543092
## All.X.glm 0.083 0.931751316 3.4543092
## All.X.bayesglm 0.148 0.931751086 0.2484433
## All.X.no.rnorm.rpart 0.080 0.727670264 0.3056880
## All.X.no.rnorm.rf 1.857 0.986994930 0.1574949
## Flu.Trend2.lm 0.003 0.903303681 0.1729956
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## MFO.lm -0.01575461 0.4080698 -0.0008029752
## Max.cor.Y.cv.0.rpart 0.00000000 0.4048928 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.74420816 0.2047780 NA
## Max.cor.Y.rpart 0.57152193 0.2650357 NA
## Max.cor.Y.lm 0.80085932 0.1806841 0.8466884911
## Interact.High.cor.Y.lm 0.88690870 0.1361616 0.8818277999
## Low.cor.X.lm 0.87993525 0.1402968 0.8979018265
## All.X.lm 0.32741401 0.3320580 0.9266370734
## All.X.glm 0.32741401 0.3320580 NA
## All.X.bayesglm 0.33330588 0.3306004 NA
## All.X.no.rnorm.rpart 0.57152193 0.2650357 NA
## All.X.no.rnorm.rf 0.78338031 0.1885728 NA
## Flu.Trend2.lm 0.85659116 0.1533303 0.9028365494
## max.Rsquared.fit min.RMSESD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.7000844 0.03621604
## Max.cor.Y.lm 0.8456678 0.01629213
## Interact.High.cor.Y.lm 0.3980416 3.98125325
## Low.cor.X.lm 0.8905275 0.02000994
## All.X.lm 0.3062242 2.96967781
## All.X.glm 0.3062242 2.96967781
## All.X.bayesglm 0.7906456 0.11070673
## All.X.no.rnorm.rpart 0.7000844 0.03621604
## All.X.no.rnorm.rf 0.9189164 0.01511178
## Flu.Trend2.lm 0.9020136 0.01355870
## max.RsquaredSD.fit min.aic.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.08730170 NA
## Max.cor.Y.lm 0.03979421 NA
## Interact.High.cor.Y.lm 0.43543342 NA
## Low.cor.X.lm 0.02855833 NA
## All.X.lm 0.52633057 NA
## All.X.glm 0.52633057 -366.7419
## All.X.bayesglm 0.17445114 -310.7405
## All.X.no.rnorm.rpart 0.08730170 NA
## All.X.no.rnorm.rf 0.02617253 NA
## Flu.Trend2.lm 0.02723253 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 158.458 168.966 10.508
## 7 fit.models_1_end 7 0 168.966 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 145.145 168.972 23.828
## 12 fit.models 7 2 168.973 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## Flu.Trend2.lm Flu.Trend2.lm lm
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.cv.0.cp.0.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.rpart ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Max.cor.Y.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log
## Interact.High.cor.Y.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA
## Low.cor.X.lm ILI.2.lag.log.nonNA, Week.bgn.last100.log, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last2.log, Week.bgn.month.fctr.nonNA
## All.X.lm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.glm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.bayesglm ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, .rnorm, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.no.rnorm.rpart ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## All.X.no.rnorm.rf ILI.2.lag.log.nonNA, Queries, Week.bgn.last100.log, Week.end.last100.log, Week.bgn.year.fctr.nonNA, Week.end.year.fctr, Week.bgn.date.fctr.nonNA, Week.end.date.fctr, Week.bgn.last10.log, Week.end.last10.log, Week.bgn.last1.log, Week.end.last1.log, Week.bgn.last2.log, Week.end.last2.log, Week.bgn.month.fctr.nonNA, Week.end.month.fctr
## Flu.Trend2.lm Queries, ILI.2.lag.log.nonNA
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 0.001602801 -0.01575461
## Max.cor.Y.cv.0.rpart 0 0.000000000 0.00000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.874259639 0.74420816
## Max.cor.Y.rpart 3 0.727670264 0.57152193
## Max.cor.Y.lm 1 0.847425566 0.80085932
## Interact.High.cor.Y.lm 1 0.888361359 0.88690870
## Low.cor.X.lm 1 0.902810393 0.87993525
## All.X.lm 1 0.931751316 0.32741401
## All.X.glm 1 0.931751316 0.32741401
## All.X.bayesglm 1 0.931751086 0.33330588
## All.X.no.rnorm.rpart 3 0.727670264 0.57152193
## All.X.no.rnorm.rf 3 0.986994930 0.78338031
## Flu.Trend2.lm 1 0.903303681 0.85659116
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.0008029752 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.7000844
## Max.cor.Y.lm 0.8466884911 0.8456678
## Interact.High.cor.Y.lm 0.8818277999 0.3980416
## Low.cor.X.lm 0.8979018265 0.8905275
## All.X.lm 0.9266370734 0.3062242
## All.X.glm NA 0.3062242
## All.X.bayesglm NA 0.7906456
## All.X.no.rnorm.rpart NA 0.7000844
## All.X.no.rnorm.rf NA 0.9189164
## Flu.Trend2.lm 0.9028365494 0.9020136
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.lm 1.7667845 500.000000
## Max.cor.Y.cv.0.rpart 1.8281536 62.500000
## Max.cor.Y.cv.0.cp.0.rpart 2.1008403 76.923077
## Max.cor.Y.rpart 0.9569378 71.428571
## Max.cor.Y.lm 1.1185682 500.000000
## Interact.High.cor.Y.lm 1.1098779 100.000000
## Low.cor.X.lm 1.1049724 100.000000
## All.X.lm 1.0235415 52.631579
## All.X.glm 0.9708738 12.048193
## All.X.bayesglm 0.5991612 6.756757
## All.X.no.rnorm.rpart 0.8920607 12.500000
## All.X.no.rnorm.rf 0.1552072 0.538503
## Flu.Trend2.lm 1.1587486 333.333333
## inv.RMSE.fit inv.RMSE.OOB inv.aic.fit
## MFO.lm 1.8067344 2.450561 NA
## Max.cor.Y.cv.0.rpart 1.8052859 2.469790 NA
## Max.cor.Y.cv.0.cp.0.rpart 5.0910651 4.883336 NA
## Max.cor.Y.rpart 3.2713097 3.773077 NA
## Max.cor.Y.lm 4.6023969 5.534520 NA
## Interact.High.cor.Y.lm 0.3643806 7.344215 NA
## Low.cor.X.lm 5.3810642 7.127746 NA
## All.X.lm 0.2894935 3.011522 NA
## All.X.glm 0.2894935 3.011522 -0.002726713
## All.X.bayesglm 4.0250626 3.024800 -0.003218119
## All.X.no.rnorm.rpart 3.2713097 3.773077 NA
## All.X.no.rnorm.rf 6.3494112 5.302993 NA
## Flu.Trend2.lm 5.7804924 6.521868 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 78 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 21 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 6 Interact.High.cor.Y.lm 0.1361616 0.88690870 0.8818277999
## 7 Low.cor.X.lm 0.1402968 0.87993525 0.8979018265
## 13 Flu.Trend2.lm 0.1533303 0.85659116 0.9028365494
## 5 Max.cor.Y.lm 0.1806841 0.80085932 0.8466884911
## 12 All.X.no.rnorm.rf 0.1885728 0.78338031 NA
## 3 Max.cor.Y.cv.0.cp.0.rpart 0.2047780 0.74420816 NA
## 4 Max.cor.Y.rpart 0.2650357 0.57152193 NA
## 11 All.X.no.rnorm.rpart 0.2650357 0.57152193 NA
## 10 All.X.bayesglm 0.3306004 0.33330588 NA
## 8 All.X.lm 0.3320580 0.32741401 0.9266370734
## 9 All.X.glm 0.3320580 0.32741401 NA
## 2 Max.cor.Y.cv.0.rpart 0.4048928 0.00000000 NA
## 1 MFO.lm 0.4080698 -0.01575461 -0.0008029752print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 5 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 29 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 7 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: Interact.High.cor.Y.lm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49287 -0.12271 -0.00814 0.09731 0.93015
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 0.018213 0.021403
## ILI.2.lag.log.nonNA 2.024306 0.449141
## Week.bgn.last100.log 0.002023 0.001367
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 0.017678 0.010910
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 0.148444 0.100875
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.211701 0.219974
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -0.230738 0.220369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.177708 0.221369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -0.260484 0.217276
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -0.243517 0.221416
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -0.234396 0.220642
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -0.073575 0.045252
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.010988 0.057602
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.025315 0.049117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -0.241012 0.050936
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -0.483300 0.081461
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 0.099225 0.098290
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -0.230973 0.097117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.091939 0.119895
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 0.148733 0.112588
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.083180 0.086770
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 0.200677 0.078522
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.067658 0.068132
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 0.163824 0.066647
## t value Pr(>|t|)
## (Intercept) 0.851 0.3953
## ILI.2.lag.log.nonNA 4.507 8.68e-06 ***
## Week.bgn.last100.log 1.480 0.1397
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 1.620 0.1059
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 1.472 0.1419
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.962 0.3364
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -1.047 0.2957
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.803 0.4226
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -1.199 0.2313
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -1.100 0.2721
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -1.062 0.2887
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -1.626 0.1048
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.191 0.8488
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.515 0.6066
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -4.732 3.11e-06 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -5.933 6.54e-09 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 1.010 0.3133
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -2.378 0.0179 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.767 0.4436
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 1.321 0.1873
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.959 0.3383
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 2.556 0.0110 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.993 0.3213
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 2.458 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1906 on 393 degrees of freedom
## Multiple R-squared: 0.8884, Adjusted R-squared: 0.8818
## F-statistic: 136 on 23 and 393 DF, p-value: < 2.2e-16## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## Week ILI Queries .src ILI.log
## 419 2012-01-08 - 2012-01-14 1.5434005 0.4993360 Test 0.43398812
## 430 2012-03-25 - 2012-03-31 1.7423860 0.3652058 Test 0.55525545
## 464 2012-11-18 - 2012-11-24 2.3046254 0.5112882 Test 0.83491815
## 445 2012-07-08 - 2012-07-14 0.9281519 0.2656042 Test -0.07455986
## 420 2012-01-15 - 2012-01-21 1.6476154 0.5006640 Test 0.49932902
## 441 2012-06-10 - 2012-06-16 1.0861211 0.2509960 Test 0.08261272
## .rnorm Week.bgn Week.end ILI.2.lag ILI.2.lag.log
## 419 -1.3166927 2012-01-08 2012-01-14 2.124130 0.75336227
## 430 -0.2262124 2012-03-25 2012-03-31 2.293422 0.83004483
## 464 -0.4158885 2012-11-18 2012-11-24 1.610915 0.47680233
## 445 0.6476146 2012-07-08 2012-07-14 1.078713 0.07576853
## 420 0.8742224 2012-01-15 2012-01-21 1.766707 0.56911730
## 441 -1.6211072 2012-06-10 2012-06-16 1.299020 0.26160987
## Week.bgn.POSIX Week.bgn.year.fctr Week.bgn.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## 441 2012-06-10 2012 06
## Week.bgn.date.fctr Week.bgn.wkday.fctr Week.bgn.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## 441 (7,13] 0 1
## Week.bgn.hour.fctr Week.bgn.minute.fctr Week.bgn.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## 441 0 0 0
## Week.end.POSIX Week.end.year.fctr Week.end.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## 441 2012-06-10 2012 06
## Week.end.date.fctr Week.end.wkday.fctr Week.end.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## 441 (7,13] 0 1
## Week.end.hour.fctr Week.end.minute.fctr Week.end.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## 441 0 0 0
## Week.bgn.zoo Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 419 1325394000 13.31265 14.00580 15.61583
## 430 1325998800 13.31265 14.00282 15.61464
## 464 1326603600 13.31265 14.00877 15.61583
## 445 1327208400 13.31265 14.00580 15.61524
## 420 1327813200 13.31265 14.00580 15.61583
## 441 1328418000 13.31265 14.00580 15.61524
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 419 17.91782 1325394000 13.31265
## 430 17.91782 1325998800 13.31265
## 464 17.91782 1326603600 13.31265
## 445 17.91782 1327208400 13.31265
## 420 17.91782 1327813200 13.31265
## 441 17.91782 1328418000 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 419 14.00580 15.61583 17.91782
## 430 14.00282 15.61464 17.91782
## 464 14.00877 15.61583 17.91782
## 445 14.00580 15.61524 17.91782
## 420 14.00580 15.61583 17.91782
## 441 14.00580 15.61524 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 419 0.75336227 2012 01
## 430 0.83004483 2012 03
## 464 0.47680233 2012 11
## 445 0.07576853 2012 07
## 420 0.56911730 2012 01
## 441 0.26160987 2012 06
## Week.bgn.date.fctr.nonNA ILI.log.predict.Interact.High.cor.Y.lm
## 419 (7,13] 0.9316064
## 430 (19,25] 0.8210195
## 464 (13,19] 0.5772443
## 445 (7,13] 0.1496508
## 420 (13,19] 0.7170902
## 441 (7,13] 0.2986348
## ILI.log.predict.Interact.High.cor.Y.lm.err
## 419 0.4976183
## 430 0.2657641
## 464 0.2576738
## 445 0.2242107
## 420 0.2177612
## 441 0.2160221predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); #sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000
## Interact.High.cor.Y.lm.importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 7
## Week ILI Queries .src ILI.log
## 419 2012-01-08 - 2012-01-14 1.5434005 0.4993360 Test 0.43398812
## 430 2012-03-25 - 2012-03-31 1.7423860 0.3652058 Test 0.55525545
## 464 2012-11-18 - 2012-11-24 2.3046254 0.5112882 Test 0.83491815
## 445 2012-07-08 - 2012-07-14 0.9281519 0.2656042 Test -0.07455986
## 420 2012-01-15 - 2012-01-21 1.6476154 0.5006640 Test 0.49932902
## .rnorm Week.bgn Week.end ILI.2.lag ILI.2.lag.log
## 419 -1.3166927 2012-01-08 2012-01-14 2.124130 0.75336227
## 430 -0.2262124 2012-03-25 2012-03-31 2.293422 0.83004483
## 464 -0.4158885 2012-11-18 2012-11-24 1.610915 0.47680233
## 445 0.6476146 2012-07-08 2012-07-14 1.078713 0.07576853
## 420 0.8742224 2012-01-15 2012-01-21 1.766707 0.56911730
## Week.bgn.POSIX Week.bgn.year.fctr Week.bgn.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## Week.bgn.date.fctr Week.bgn.wkday.fctr Week.bgn.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## Week.bgn.hour.fctr Week.bgn.minute.fctr Week.bgn.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## Week.end.POSIX Week.end.year.fctr Week.end.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## Week.end.date.fctr Week.end.wkday.fctr Week.end.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## Week.end.hour.fctr Week.end.minute.fctr Week.end.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## Week.bgn.zoo Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 419 1325394000 13.31265 14.00580 15.61583
## 430 1325998800 13.31265 14.00282 15.61464
## 464 1326603600 13.31265 14.00877 15.61583
## 445 1327208400 13.31265 14.00580 15.61524
## 420 1327813200 13.31265 14.00580 15.61583
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 419 17.91782 1325394000 13.31265
## 430 17.91782 1325998800 13.31265
## 464 17.91782 1326603600 13.31265
## 445 17.91782 1327208400 13.31265
## 420 17.91782 1327813200 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 419 14.00580 15.61583 17.91782
## 430 14.00282 15.61464 17.91782
## 464 14.00877 15.61583 17.91782
## 445 14.00580 15.61524 17.91782
## 420 14.00580 15.61583 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 419 0.75336227 2012 01
## 430 0.83004483 2012 03
## 464 0.47680233 2012 11
## 445 0.07576853 2012 07
## 420 0.56911730 2012 01
## Week.bgn.date.fctr.nonNA ILI.log.predict.Interact.High.cor.Y.lm
## 419 (7,13] 0.9316064
## 430 (19,25] 0.8210195
## 464 (13,19] 0.5772443
## 445 (7,13] 0.1496508
## 420 (13,19] 0.7170902
## ILI.log.predict.Interact.High.cor.Y.lm.err
## 419 0.4976183
## 430 0.2657641
## 464 0.2576738
## 445 0.2242107
## 420 0.2177612
## ILI.log.predict.Interact.High.cor.Y.lm.accurate
## 419 FALSE
## 430 FALSE
## 464 FALSE
## 445 FALSE
## 420 FALSE
## .label
## 419 2012-01-08 - 2012-01-14
## 430 2012-03-25 - 2012-03-31
## 464 2012-11-18 - 2012-11-24
## 445 2012-07-08 - 2012-07-14
## 420 2012-01-15 - 2012-01-21
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 168.973 180.142 11.169
## 13 fit.models 7 3 180.142 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "Week.bgn.wkday.fctr" "Week.bgn.wkend" "Week.bgn.hour.fctr"
## [4] "Week.bgn.minute.fctr" "Week.bgn.second.fctr" "Week.end.wkday.fctr"
## [7] "Week.end.wkend" "Week.end.hour.fctr" "Week.end.minute.fctr"
## [10] "Week.end.second.fctr"print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## [1] "Week.bgn.wkday.fctr" "Week.bgn.wkend" "Week.bgn.hour.fctr"
## [4] "Week.bgn.minute.fctr" "Week.bgn.second.fctr" "Week.end.wkday.fctr"
## [7] "Week.end.wkend" "Week.end.hour.fctr" "Week.end.minute.fctr"
## [10] "Week.end.second.fctr"print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "Week.bgn.wkday.fctr"
## [2] "Week.bgn.wkend"
## [3] "Week.bgn.hour.fctr"
## [4] "Week.bgn.minute.fctr"
## [5] "Week.bgn.second.fctr"
## [6] "Week.end.wkday.fctr"
## [7] "Week.end.wkend"
## [8] "Week.end.hour.fctr"
## [9] "Week.end.minute.fctr"
## [10] "Week.end.second.fctr"
## [11] "ILI.log.predict.Interact.High.cor.Y.lm"
## [12] "ILI.log.predict.Interact.High.cor.Y.lm.err"
## [13] "ILI.log.predict.Interact.High.cor.Y.lm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 180.142 184.107 3.965
## 14 fit.data.training 8 0 184.108 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.lm"
## [1] " indep_vars: ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49287 -0.12271 -0.00814 0.09731 0.93015
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 0.018213 0.021403
## ILI.2.lag.log.nonNA 2.024306 0.449141
## Week.bgn.last100.log 0.002023 0.001367
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 0.017678 0.010910
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 0.148444 0.100875
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.211701 0.219974
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -0.230738 0.220369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.177708 0.221369
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -0.260484 0.217276
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -0.243517 0.221416
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -0.234396 0.220642
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -0.073575 0.045252
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.010988 0.057602
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.025315 0.049117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -0.241012 0.050936
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -0.483300 0.081461
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 0.099225 0.098290
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -0.230973 0.097117
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.091939 0.119895
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 0.148733 0.112588
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.083180 0.086770
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 0.200677 0.078522
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.067658 0.068132
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 0.163824 0.066647
## t value Pr(>|t|)
## (Intercept) 0.851 0.3953
## ILI.2.lag.log.nonNA 4.507 8.68e-06 ***
## Week.bgn.last100.log 1.480 0.1397
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 1.620 0.1059
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 1.472 0.1419
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` -0.962 0.3364
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` -1.047 0.2957
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` -0.803 0.4226
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` -1.199 0.2313
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` -1.100 0.2721
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` -1.062 0.2887
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA9` NA NA
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` -1.626 0.1048
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` -0.191 0.8488
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 0.515 0.6066
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` -4.732 3.11e-06 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` -5.933 6.54e-09 ***
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 1.010 0.3133
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` -2.378 0.0179 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 0.767 0.4436
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 1.321 0.1873
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 0.959 0.3383
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 2.556 0.0110 *
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` -0.993 0.3213
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 2.458 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1906 on 393 degrees of freedom
## Multiple R-squared: 0.8884, Adjusted R-squared: 0.8818
## F-statistic: 136 on 23 and 393 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Final.lm lm
## feats
## 1 ILI.2.lag.log.nonNA, Week.bgn.last100.log, ILI.2.lag.log.nonNA:ILI.2.lag.log.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last100.log, ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA, ILI.2.lag.log.nonNA:Week.bgn.last2.log, ILI.2.lag.log.nonNA:Week.bgn.last1.log, ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.922 0.01
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.Rsquared.fit
## 1 0.8883614 2.744383 0.8818278 0.3980416
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.981253 0.4354334rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 184.108 188.576 4.468
## 15 fit.data.training 8 1 188.577 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## Week ILI Queries .src ILI.log .rnorm
## 278 2009-04-26 - 2009-05-02 2.981589 0.5553810 Train 1.0924564 0.05420224
## 296 2009-08-30 - 2009-09-05 3.719694 0.4116866 Train 1.3136413 -1.70337580
## 295 2009-08-23 - 2009-08-29 2.471660 0.3466135 Train 0.9048899 -0.83932764
## 279 2009-05-03 - 2009-05-09 2.437022 0.5551129 Train 0.8907770 0.76701532
## 213 2008-01-27 - 2008-02-02 4.433810 0.4143426 Train 1.4892593 1.83754480
## 282 2009-05-24 - 2009-05-30 4.213152 0.2948207 Train 1.4382111 0.58885757
## Week.bgn Week.end ILI.2.lag ILI.2.lag.log Week.bgn.POSIX
## 278 2009-04-26 2009-05-02 1.292327 0.2564443 2009-04-26
## 296 2009-08-30 2009-09-05 1.641071 0.4953493 2009-08-30
## 295 2009-08-23 2009-08-29 1.161419 0.1496424 2009-08-23
## 279 2009-05-03 2009-05-09 1.271641 0.2403083 2009-05-03
## 213 2008-01-27 2008-02-02 2.359343 0.8583831 2008-01-27
## 282 2009-05-24 2009-05-30 2.281301 0.8247459 2009-05-24
## Week.bgn.year.fctr Week.bgn.month.fctr Week.bgn.date.fctr
## 278 2009 04 (25,31]
## 296 2009 08 (25,31]
## 295 2009 08 (19,25]
## 279 2009 05 (0.97,7]
## 213 2008 01 (25,31]
## 282 2009 05 (19,25]
## Week.bgn.wkday.fctr Week.bgn.wkend Week.bgn.hour.fctr
## 278 0 1 0
## 296 0 1 0
## 295 0 1 0
## 279 0 1 0
## 213 0 1 0
## 282 0 1 0
## Week.bgn.minute.fctr Week.bgn.second.fctr Week.end.POSIX
## 278 0 0 2009-04-26
## 296 0 0 2009-08-30
## 295 0 0 2009-08-23
## 279 0 0 2009-05-03
## 213 0 0 2008-01-27
## 282 0 0 2009-05-24
## Week.end.year.fctr Week.end.month.fctr Week.end.date.fctr
## 278 2009 04 (25,31]
## 296 2009 08 (25,31]
## 295 2009 08 (19,25]
## 279 2009 05 (0.97,7]
## 213 2008 01 (25,31]
## 282 2009 05 (19,25]
## Week.end.wkday.fctr Week.end.wkend Week.end.hour.fctr
## 278 0 1 0
## 296 0 1 0
## 295 0 1 0
## 279 0 1 0
## 213 0 1 0
## 282 0 1 0
## Week.end.minute.fctr Week.end.second.fctr Week.bgn.zoo
## 278 0 0 1073192400
## 296 0 0 1073797200
## 295 0 0 1074402000
## 279 0 0 1075006800
## 213 0 0 1075611600
## 282 0 0 1076216400
## Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 278 13.31265 14.0058 15.61464
## 296 13.31265 14.0058 15.61524
## 295 13.31265 14.0058 15.61524
## 279 13.31265 14.0058 15.61464
## 213 13.31265 14.0058 15.61524
## 282 13.31265 14.0058 15.61524
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 278 17.91782 1073192400 13.31265
## 296 17.91782 1073797200 13.31265
## 295 17.91782 1074402000 13.31265
## 279 17.91782 1075006800 13.31265
## 213 17.91782 1075611600 13.31265
## 282 17.91782 1076216400 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 278 14.0058 15.61464 17.91782
## 296 14.0058 15.61524 17.91782
## 295 14.0058 15.61524 17.91782
## 279 14.0058 15.61464 17.91782
## 213 14.0058 15.61524 17.91782
## 282 14.0058 15.61524 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 278 0.2564443 2009 04
## 296 0.4953493 2009 08
## 295 0.1496424 2009 08
## 279 0.2403083 2009 05
## 213 0.8583831 2008 01
## 282 0.8247459 2009 05
## Week.bgn.date.fctr.nonNA ILI.log.predict.Final.lm
## 278 (25,31] 0.1623063
## 296 (25,31] 0.5758465
## 295 (19,25] 0.2119733
## 279 (0.97,7] 0.2955065
## 213 (25,31] 0.9013407
## 282 (19,25] 0.8817212
## ILI.log.predict.Final.lm.err
## 278 0.9301501
## 296 0.7377949
## 295 0.6929166
## 279 0.5952705
## 213 0.5879186
## 282 0.5564899sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Interact.High.cor.Y.lm.importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000
## importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000
## Final.lm.importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 7
## Week ILI Queries .src ILI.log .rnorm
## 278 2009-04-26 - 2009-05-02 2.981589 0.5553810 Train 1.0924564 0.05420224
## 296 2009-08-30 - 2009-09-05 3.719694 0.4116866 Train 1.3136413 -1.70337580
## 295 2009-08-23 - 2009-08-29 2.471660 0.3466135 Train 0.9048899 -0.83932764
## 279 2009-05-03 - 2009-05-09 2.437022 0.5551129 Train 0.8907770 0.76701532
## 213 2008-01-27 - 2008-02-02 4.433810 0.4143426 Train 1.4892593 1.83754480
## Week.bgn Week.end ILI.2.lag ILI.2.lag.log Week.bgn.POSIX
## 278 2009-04-26 2009-05-02 1.292327 0.2564443 2009-04-26
## 296 2009-08-30 2009-09-05 1.641071 0.4953493 2009-08-30
## 295 2009-08-23 2009-08-29 1.161419 0.1496424 2009-08-23
## 279 2009-05-03 2009-05-09 1.271641 0.2403083 2009-05-03
## 213 2008-01-27 2008-02-02 2.359343 0.8583831 2008-01-27
## Week.bgn.year.fctr Week.bgn.month.fctr Week.bgn.date.fctr
## 278 2009 04 (25,31]
## 296 2009 08 (25,31]
## 295 2009 08 (19,25]
## 279 2009 05 (0.97,7]
## 213 2008 01 (25,31]
## Week.bgn.wkday.fctr Week.bgn.wkend Week.bgn.hour.fctr
## 278 0 1 0
## 296 0 1 0
## 295 0 1 0
## 279 0 1 0
## 213 0 1 0
## Week.bgn.minute.fctr Week.bgn.second.fctr Week.end.POSIX
## 278 0 0 2009-04-26
## 296 0 0 2009-08-30
## 295 0 0 2009-08-23
## 279 0 0 2009-05-03
## 213 0 0 2008-01-27
## Week.end.year.fctr Week.end.month.fctr Week.end.date.fctr
## 278 2009 04 (25,31]
## 296 2009 08 (25,31]
## 295 2009 08 (19,25]
## 279 2009 05 (0.97,7]
## 213 2008 01 (25,31]
## Week.end.wkday.fctr Week.end.wkend Week.end.hour.fctr
## 278 0 1 0
## 296 0 1 0
## 295 0 1 0
## 279 0 1 0
## 213 0 1 0
## Week.end.minute.fctr Week.end.second.fctr Week.bgn.zoo
## 278 0 0 1073192400
## 296 0 0 1073797200
## 295 0 0 1074402000
## 279 0 0 1075006800
## 213 0 0 1075611600
## Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 278 13.31265 14.0058 15.61464
## 296 13.31265 14.0058 15.61524
## 295 13.31265 14.0058 15.61524
## 279 13.31265 14.0058 15.61464
## 213 13.31265 14.0058 15.61524
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 278 17.91782 1073192400 13.31265
## 296 17.91782 1073797200 13.31265
## 295 17.91782 1074402000 13.31265
## 279 17.91782 1075006800 13.31265
## 213 17.91782 1075611600 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 278 14.0058 15.61464 17.91782
## 296 14.0058 15.61524 17.91782
## 295 14.0058 15.61524 17.91782
## 279 14.0058 15.61464 17.91782
## 213 14.0058 15.61524 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 278 0.2564443 2009 04
## 296 0.4953493 2009 08
## 295 0.1496424 2009 08
## 279 0.2403083 2009 05
## 213 0.8583831 2008 01
## Week.bgn.date.fctr.nonNA ILI.log.predict.Final.lm
## 278 (25,31] 0.1623063
## 296 (25,31] 0.5758465
## 295 (19,25] 0.2119733
## 279 (0.97,7] 0.2955065
## 213 (25,31] 0.9013407
## ILI.log.predict.Final.lm.err .label
## 278 0.9301501 2009-04-26 - 2009-05-02
## 296 0.7377949 2009-08-30 - 2009-09-05
## 295 0.6929166 2009-08-23 - 2009-08-29
## 279 0.5952705 2009-05-03 - 2009-05-09
## 213 0.5879186 2008-01-27 - 2008-02-02
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "ILI.log.predict.Final.lm" "ILI.log.predict.Final.lm.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 188.577 193.019 4.442
## 16 predict.data.new 9 0 193.020 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## Week ILI Queries .src ILI.log
## 419 2012-01-08 - 2012-01-14 1.5434005 0.4993360 Test 0.43398812
## 430 2012-03-25 - 2012-03-31 1.7423860 0.3652058 Test 0.55525545
## 464 2012-11-18 - 2012-11-24 2.3046254 0.5112882 Test 0.83491815
## 445 2012-07-08 - 2012-07-14 0.9281519 0.2656042 Test -0.07455986
## 420 2012-01-15 - 2012-01-21 1.6476154 0.5006640 Test 0.49932902
## 441 2012-06-10 - 2012-06-16 1.0861211 0.2509960 Test 0.08261272
## .rnorm Week.bgn Week.end ILI.2.lag ILI.2.lag.log
## 419 -1.3166927 2012-01-08 2012-01-14 2.124130 0.75336227
## 430 -0.2262124 2012-03-25 2012-03-31 2.293422 0.83004483
## 464 -0.4158885 2012-11-18 2012-11-24 1.610915 0.47680233
## 445 0.6476146 2012-07-08 2012-07-14 1.078713 0.07576853
## 420 0.8742224 2012-01-15 2012-01-21 1.766707 0.56911730
## 441 -1.6211072 2012-06-10 2012-06-16 1.299020 0.26160987
## Week.bgn.POSIX Week.bgn.year.fctr Week.bgn.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## 441 2012-06-10 2012 06
## Week.bgn.date.fctr Week.bgn.wkday.fctr Week.bgn.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## 441 (7,13] 0 1
## Week.bgn.hour.fctr Week.bgn.minute.fctr Week.bgn.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## 441 0 0 0
## Week.end.POSIX Week.end.year.fctr Week.end.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## 441 2012-06-10 2012 06
## Week.end.date.fctr Week.end.wkday.fctr Week.end.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## 441 (7,13] 0 1
## Week.end.hour.fctr Week.end.minute.fctr Week.end.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## 441 0 0 0
## Week.bgn.zoo Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 419 1325394000 13.31265 14.00580 15.61583
## 430 1325998800 13.31265 14.00282 15.61464
## 464 1326603600 13.31265 14.00877 15.61583
## 445 1327208400 13.31265 14.00580 15.61524
## 420 1327813200 13.31265 14.00580 15.61583
## 441 1328418000 13.31265 14.00580 15.61524
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 419 17.91782 1325394000 13.31265
## 430 17.91782 1325998800 13.31265
## 464 17.91782 1326603600 13.31265
## 445 17.91782 1327208400 13.31265
## 420 17.91782 1327813200 13.31265
## 441 17.91782 1328418000 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 419 14.00580 15.61583 17.91782
## 430 14.00282 15.61464 17.91782
## 464 14.00877 15.61583 17.91782
## 445 14.00580 15.61524 17.91782
## 420 14.00580 15.61583 17.91782
## 441 14.00580 15.61524 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 419 0.75336227 2012 01
## 430 0.83004483 2012 03
## 464 0.47680233 2012 11
## 445 0.07576853 2012 07
## 420 0.56911730 2012 01
## 441 0.26160987 2012 06
## Week.bgn.date.fctr.nonNA ILI.log.predict.Final.lm
## 419 (7,13] 0.9316064
## 430 (19,25] 0.8210195
## 464 (13,19] 0.5772443
## 445 (7,13] 0.1496508
## 420 (13,19] 0.7170902
## 441 (7,13] 0.2986348
## ILI.log.predict.Final.lm.err
## 419 0.4976183
## 430 0.2657641
## 464 0.2576738
## 445 0.2242107
## 420 0.2177612
## 441 0.2160221if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 7
## Week ILI Queries .src ILI.log
## 419 2012-01-08 - 2012-01-14 1.5434005 0.4993360 Test 0.43398812
## 430 2012-03-25 - 2012-03-31 1.7423860 0.3652058 Test 0.55525545
## 464 2012-11-18 - 2012-11-24 2.3046254 0.5112882 Test 0.83491815
## 445 2012-07-08 - 2012-07-14 0.9281519 0.2656042 Test -0.07455986
## 420 2012-01-15 - 2012-01-21 1.6476154 0.5006640 Test 0.49932902
## .rnorm Week.bgn Week.end ILI.2.lag ILI.2.lag.log
## 419 -1.3166927 2012-01-08 2012-01-14 2.124130 0.75336227
## 430 -0.2262124 2012-03-25 2012-03-31 2.293422 0.83004483
## 464 -0.4158885 2012-11-18 2012-11-24 1.610915 0.47680233
## 445 0.6476146 2012-07-08 2012-07-14 1.078713 0.07576853
## 420 0.8742224 2012-01-15 2012-01-21 1.766707 0.56911730
## Week.bgn.POSIX Week.bgn.year.fctr Week.bgn.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## Week.bgn.date.fctr Week.bgn.wkday.fctr Week.bgn.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## Week.bgn.hour.fctr Week.bgn.minute.fctr Week.bgn.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## Week.end.POSIX Week.end.year.fctr Week.end.month.fctr
## 419 2012-01-08 2012 01
## 430 2012-03-25 2012 03
## 464 2012-11-18 2012 11
## 445 2012-07-08 2012 07
## 420 2012-01-15 2012 01
## Week.end.date.fctr Week.end.wkday.fctr Week.end.wkend
## 419 (7,13] 0 1
## 430 (19,25] 0 1
## 464 (13,19] 0 1
## 445 (7,13] 0 1
## 420 (13,19] 0 1
## Week.end.hour.fctr Week.end.minute.fctr Week.end.second.fctr
## 419 0 0 0
## 430 0 0 0
## 464 0 0 0
## 445 0 0 0
## 420 0 0 0
## Week.bgn.zoo Week.bgn.last1.log Week.bgn.last2.log Week.bgn.last10.log
## 419 1325394000 13.31265 14.00580 15.61583
## 430 1325998800 13.31265 14.00282 15.61464
## 464 1326603600 13.31265 14.00877 15.61583
## 445 1327208400 13.31265 14.00580 15.61524
## 420 1327813200 13.31265 14.00580 15.61583
## Week.bgn.last100.log Week.end.zoo Week.end.last1.log
## 419 17.91782 1325394000 13.31265
## 430 17.91782 1325998800 13.31265
## 464 17.91782 1326603600 13.31265
## 445 17.91782 1327208400 13.31265
## 420 17.91782 1327813200 13.31265
## Week.end.last2.log Week.end.last10.log Week.end.last100.log
## 419 14.00580 15.61583 17.91782
## 430 14.00282 15.61464 17.91782
## 464 14.00877 15.61583 17.91782
## 445 14.00580 15.61524 17.91782
## 420 14.00580 15.61583 17.91782
## ILI.2.lag.log.nonNA Week.bgn.year.fctr.nonNA Week.bgn.month.fctr.nonNA
## 419 0.75336227 2012 01
## 430 0.83004483 2012 03
## 464 0.47680233 2012 11
## 445 0.07576853 2012 07
## 420 0.56911730 2012 01
## Week.bgn.date.fctr.nonNA ILI.log.predict.Final.lm
## 419 (7,13] 0.9316064
## 430 (19,25] 0.8210195
## 464 (13,19] 0.5772443
## 445 (7,13] 0.1496508
## 420 (13,19] 0.7170902
## ILI.log.predict.Final.lm.err .label
## 419 0.4976183 2012-01-08 - 2012-01-14
## 430 0.2657641 2012-03-25 - 2012-03-31
## 464 0.2576738 2012-11-18 - 2012-11-24
## 445 0.2242107 2012-07-08 - 2012-07-14
## 420 0.2177612 2012-01-15 - 2012-01-21
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Interact.High.cor.Y.lm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.lm"print(dim(glb_fitobs_df))## [1] 417 42print(dsp_models_df)## model_id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 6 Interact.High.cor.Y.lm 0.1361616 0.88690870 0.8818277999
## 7 Low.cor.X.lm 0.1402968 0.87993525 0.8979018265
## 13 Flu.Trend2.lm 0.1533303 0.85659116 0.9028365494
## 5 Max.cor.Y.lm 0.1806841 0.80085932 0.8466884911
## 12 All.X.no.rnorm.rf 0.1885728 0.78338031 NA
## 3 Max.cor.Y.cv.0.cp.0.rpart 0.2047780 0.74420816 NA
## 4 Max.cor.Y.rpart 0.2650357 0.57152193 NA
## 11 All.X.no.rnorm.rpart 0.2650357 0.57152193 NA
## 10 All.X.bayesglm 0.3306004 0.33330588 NA
## 8 All.X.lm 0.3320580 0.32741401 0.9266370734
## 9 All.X.glm 0.3320580 0.32741401 NA
## 2 Max.cor.Y.cv.0.rpart 0.4048928 0.00000000 NA
## 1 MFO.lm 0.4080698 -0.01575461 -0.0008029752if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
} ## [1] "Interact.High.cor.Y.lm OOB RMSE: 0.1362"## Warning: contrasts dropped from factor Week.bgn.year.fctr.nonNA## Warning: contrasts dropped from factor Week.bgn.month.fctr.nonNA## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] "Final.lm prediction stats for glb_newobs_df:"
## model_id max.R.sq.new min.RMSE.new
## 1 Final.lm 0.8869087 0.1361616if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## Interact.High.cor.Y.lm.importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000
## importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000
## Final.lm.importance
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA4` 100.000000
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA3` 79.081237
## ILI.2.lag.log.nonNA 75.169274
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA10` 41.185653
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA12` 39.486108
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA6` 38.096300
## `ILI.2.lag.log.nonNA:Week.bgn.last2.log` 24.993131
## `ILI.2.lag.log.nonNA:Week.bgn.last100.log` 24.897758
## Week.bgn.last100.log 22.448374
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA2` 22.305442
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA8` 19.684026
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA6` 17.556280
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA7` 15.831464
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA8` 15.178728
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA4` 14.912604
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA5` 14.258897
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA11` 13.972025
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA3` 13.438086
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA9` 13.372715
## `ILI.2.lag.log.nonNA:Week.bgn.year.fctr.nonNA5` 10.658250
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA7` 10.032455
## `ILI.2.lag.log.nonNA:Week.bgn.month.fctr.nonNA2` 5.653516
## `ILI.2.lag.log.nonNA:Week.bgn.last1.log` 0.000000# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 193.020 197.287 4.267
## 17 display.session.info 10 0 197.288 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 10.224 109.764 99.540
## 11 fit.models 7 1 145.145 168.972 23.828
## 6 cluster.data 4 0 109.765 127.266 17.501
## 10 fit.models 7 0 129.347 145.145 15.798
## 12 fit.models 7 2 168.973 180.142 11.169
## 14 fit.data.training 8 0 184.108 188.576 4.468
## 15 fit.data.training 8 1 188.577 193.019 4.442
## 16 predict.data.new 9 0 193.020 197.287 4.267
## 13 fit.models 7 3 180.142 184.107 3.965
## 2 inspect.data 2 0 7.173 9.516 2.343
## 7 manage.missing.data 4 1 127.267 128.257 0.990
## 8 select.features 5 0 128.258 128.999 0.742
## 3 scrub.data 2 1 9.516 10.161 0.645
## 9 partition.data.training 6 0 129.000 129.347 0.347
## 1 import.data 1 0 6.863 7.173 0.310
## 4 transform.data 2 2 10.161 10.224 0.063
## duration
## 5 99.540
## 11 23.827
## 6 17.501
## 10 15.798
## 12 11.169
## 14 4.468
## 15 4.442
## 16 4.267
## 13 3.965
## 2 2.343
## 7 0.990
## 8 0.741
## 3 0.645
## 9 0.347
## 1 0.310
## 4 0.063
## [1] "Total Elapsed Time: 197.287 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] gdata_2.16.1 randomForest_4.6-10 arm_1.8-5
## [4] lme4_1.1-7 Matrix_1.2-1 MASS_7.3-40
## [7] rpart.plot_1.5.2 rpart_4.1-9 reshape2_1.4.1
## [10] mice_2.22 Rcpp_0.11.6 XML_3.98-1.2
## [13] dplyr_0.4.1 plyr_1.8.2 zoo_1.7-12
## [16] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [19] doBy_4.5-13 survival_2.38-1 caret_6.0-47
## [22] ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] gtools_3.5.0 splines_3.2.0 colorspace_1.2-6
## [4] htmltools_0.2.6 yaml_2.1.13 mgcv_1.8-6
## [7] nloptr_1.0.4 DBI_0.3.1 RColorBrewer_1.1-2
## [10] stringr_1.0.0 munsell_0.4.2 gtable_0.1.2
## [13] codetools_0.2-11 coda_0.17-1 evaluate_0.7
## [16] labeling_0.3 knitr_1.10.5 SparseM_1.6
## [19] quantreg_5.11 pbkrtest_0.4-2 proto_0.3-10
## [22] scales_0.2.4 formatR_1.2 BradleyTerry2_1.0-6
## [25] abind_1.4-3 digest_0.6.8 stringi_0.4-1
## [28] brglm_0.5-9 tools_3.2.0 magrittr_1.5
## [31] lazyeval_0.1.10 car_2.0-25 assertthat_0.1
## [34] minqa_1.2.4 rmarkdown_0.6.1 nnet_7.3-9
## [37] nlme_3.1-120 compiler_3.2.0